{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "pd.set_option('display.max_columns', 100)\n",
    "pd.set_option('display.max_rows', 200)\n",
    "import numpy as np \n",
    "from scipy.stats import skew\n",
    "import scipy.stats as stats \n",
    "\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1460, 81)\n",
      "(1459, 80)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>196.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>706.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>856.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>856</td>\n",
       "      <td>854</td>\n",
       "      <td>0</td>\n",
       "      <td>1710</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>8</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2.0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>978.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>1262.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1262</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>162.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Mn</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>486.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>920.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>0</td>\n",
       "      <td>1786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2.0</td>\n",
       "      <td>608.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Shng</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>BrkTil</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>216.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>756.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>961</td>\n",
       "      <td>756</td>\n",
       "      <td>0</td>\n",
       "      <td>1717</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>3.0</td>\n",
       "      <td>642.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>350.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Av</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>655.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>0</td>\n",
       "      <td>2198</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>9</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>3.0</td>\n",
       "      <td>836.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities LotConfig LandSlope Neighborhood Condition1  \\\n",
       "0         Lvl    AllPub    Inside       Gtl      CollgCr       Norm   \n",
       "1         Lvl    AllPub       FR2       Gtl      Veenker      Feedr   \n",
       "2         Lvl    AllPub    Inside       Gtl      CollgCr       Norm   \n",
       "3         Lvl    AllPub    Corner       Gtl      Crawfor       Norm   \n",
       "4         Lvl    AllPub       FR2       Gtl      NoRidge       Norm   \n",
       "\n",
       "  Condition2 BldgType HouseStyle  OverallQual  OverallCond  YearBuilt  \\\n",
       "0       Norm     1Fam     2Story            7            5       2003   \n",
       "1       Norm     1Fam     1Story            6            8       1976   \n",
       "2       Norm     1Fam     2Story            7            5       2001   \n",
       "3       Norm     1Fam     2Story            7            5       1915   \n",
       "4       Norm     1Fam     2Story            8            5       2000   \n",
       "\n",
       "   YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType  \\\n",
       "0          2003     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "1          1976     Gable  CompShg     MetalSd     MetalSd       None   \n",
       "2          2002     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "3          1970     Gable  CompShg     Wd Sdng     Wd Shng       None   \n",
       "4          2000     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "\n",
       "   MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure  \\\n",
       "0       196.0        Gd        TA      PConc       Gd       TA           No   \n",
       "1         0.0        TA        TA     CBlock       Gd       TA           Gd   \n",
       "2       162.0        Gd        TA      PConc       Gd       TA           Mn   \n",
       "3         0.0        TA        TA     BrkTil       TA       Gd           No   \n",
       "4       350.0        Gd        TA      PConc       Gd       TA           Av   \n",
       "\n",
       "  BsmtFinType1  BsmtFinSF1 BsmtFinType2  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  \\\n",
       "0          GLQ       706.0          Unf         0.0      150.0        856.0   \n",
       "1          ALQ       978.0          Unf         0.0      284.0       1262.0   \n",
       "2          GLQ       486.0          Unf         0.0      434.0        920.0   \n",
       "3          ALQ       216.0          Unf         0.0      540.0        756.0   \n",
       "4          GLQ       655.0          Unf         0.0      490.0       1145.0   \n",
       "\n",
       "  Heating HeatingQC CentralAir Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  \\\n",
       "0    GasA        Ex          Y      SBrkr       856       854             0   \n",
       "1    GasA        Ex          Y      SBrkr      1262         0             0   \n",
       "2    GasA        Ex          Y      SBrkr       920       866             0   \n",
       "3    GasA        Gd          Y      SBrkr       961       756             0   \n",
       "4    GasA        Ex          Y      SBrkr      1145      1053             0   \n",
       "\n",
       "   GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  \\\n",
       "0       1710           1.0           0.0         2         1             3   \n",
       "1       1262           0.0           1.0         2         0             3   \n",
       "2       1786           1.0           0.0         2         1             3   \n",
       "3       1717           1.0           0.0         1         0             3   \n",
       "4       2198           1.0           0.0         2         1             4   \n",
       "\n",
       "   KitchenAbvGr KitchenQual  TotRmsAbvGrd Functional  Fireplaces FireplaceQu  \\\n",
       "0             1          Gd             8        Typ           0         NaN   \n",
       "1             1          TA             6        Typ           1          TA   \n",
       "2             1          Gd             6        Typ           1          TA   \n",
       "3             1          Gd             7        Typ           1          Gd   \n",
       "4             1          Gd             9        Typ           1          TA   \n",
       "\n",
       "  GarageType  GarageYrBlt GarageFinish  GarageCars  GarageArea GarageQual  \\\n",
       "0     Attchd       2003.0          RFn         2.0       548.0         TA   \n",
       "1     Attchd       1976.0          RFn         2.0       460.0         TA   \n",
       "2     Attchd       2001.0          RFn         2.0       608.0         TA   \n",
       "3     Detchd       1998.0          Unf         3.0       642.0         TA   \n",
       "4     Attchd       2000.0          RFn         3.0       836.0         TA   \n",
       "\n",
       "  GarageCond PavedDrive  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  \\\n",
       "0         TA          Y           0           61              0          0   \n",
       "1         TA          Y         298            0              0          0   \n",
       "2         TA          Y           0           42              0          0   \n",
       "3         TA          Y           0           35            272          0   \n",
       "4         TA          Y         192           84              0          0   \n",
       "\n",
       "   ScreenPorch  PoolArea PoolQC Fence MiscFeature  MiscVal  MoSold  YrSold  \\\n",
       "0            0         0    NaN   NaN         NaN        0       2    2008   \n",
       "1            0         0    NaN   NaN         NaN        0       5    2007   \n",
       "2            0         0    NaN   NaN         NaN        0       9    2008   \n",
       "3            0         0    NaN   NaN         NaN        0       2    2006   \n",
       "4            0         0    NaN   NaN         NaN        0      12    2008   \n",
       "\n",
       "  SaleType SaleCondition  SalePrice  \n",
       "0       WD        Normal   208500.0  \n",
       "1       WD        Normal   181500.0  \n",
       "2       WD        Normal   223500.0  \n",
       "3       WD       Abnorml   140000.0  \n",
       "4       WD        Normal   250000.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat(objs=[train, test], axis=0, sort=False, ignore_index=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2433.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2896.000000</td>\n",
       "      <td>2918.000000</td>\n",
       "      <td>2918.000000</td>\n",
       "      <td>2918.000000</td>\n",
       "      <td>2918.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2917.000000</td>\n",
       "      <td>2917.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2760.000000</td>\n",
       "      <td>2918.000000</td>\n",
       "      <td>2918.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>57.137718</td>\n",
       "      <td>69.305795</td>\n",
       "      <td>10168.114080</td>\n",
       "      <td>6.089072</td>\n",
       "      <td>5.564577</td>\n",
       "      <td>1971.312778</td>\n",
       "      <td>1984.264474</td>\n",
       "      <td>102.201312</td>\n",
       "      <td>441.423235</td>\n",
       "      <td>49.582248</td>\n",
       "      <td>560.772104</td>\n",
       "      <td>1051.777587</td>\n",
       "      <td>1159.581706</td>\n",
       "      <td>336.483727</td>\n",
       "      <td>4.694416</td>\n",
       "      <td>1500.759849</td>\n",
       "      <td>0.429894</td>\n",
       "      <td>0.061364</td>\n",
       "      <td>1.568003</td>\n",
       "      <td>0.380267</td>\n",
       "      <td>2.860226</td>\n",
       "      <td>1.044536</td>\n",
       "      <td>6.451524</td>\n",
       "      <td>0.597122</td>\n",
       "      <td>1978.113406</td>\n",
       "      <td>1.766621</td>\n",
       "      <td>472.874572</td>\n",
       "      <td>93.709832</td>\n",
       "      <td>47.486811</td>\n",
       "      <td>23.098321</td>\n",
       "      <td>2.602261</td>\n",
       "      <td>16.062350</td>\n",
       "      <td>2.251799</td>\n",
       "      <td>50.825968</td>\n",
       "      <td>6.213087</td>\n",
       "      <td>2007.792737</td>\n",
       "      <td>180921.195890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>842.787043</td>\n",
       "      <td>42.517628</td>\n",
       "      <td>23.344905</td>\n",
       "      <td>7886.996359</td>\n",
       "      <td>1.409947</td>\n",
       "      <td>1.113131</td>\n",
       "      <td>30.291442</td>\n",
       "      <td>20.894344</td>\n",
       "      <td>179.334253</td>\n",
       "      <td>455.610826</td>\n",
       "      <td>169.205611</td>\n",
       "      <td>439.543659</td>\n",
       "      <td>440.766258</td>\n",
       "      <td>392.362079</td>\n",
       "      <td>428.701456</td>\n",
       "      <td>46.396825</td>\n",
       "      <td>506.051045</td>\n",
       "      <td>0.524736</td>\n",
       "      <td>0.245687</td>\n",
       "      <td>0.552969</td>\n",
       "      <td>0.502872</td>\n",
       "      <td>0.822693</td>\n",
       "      <td>0.214462</td>\n",
       "      <td>1.569379</td>\n",
       "      <td>0.646129</td>\n",
       "      <td>25.574285</td>\n",
       "      <td>0.761624</td>\n",
       "      <td>215.394815</td>\n",
       "      <td>126.526589</td>\n",
       "      <td>67.575493</td>\n",
       "      <td>64.244246</td>\n",
       "      <td>25.188169</td>\n",
       "      <td>56.184365</td>\n",
       "      <td>35.663946</td>\n",
       "      <td>567.402211</td>\n",
       "      <td>2.714762</td>\n",
       "      <td>1.314964</td>\n",
       "      <td>79442.502883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1872.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>334.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>334.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1895.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>34900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>730.500000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>7478.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1953.500000</td>\n",
       "      <td>1965.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>793.000000</td>\n",
       "      <td>876.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1960.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>129975.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>9453.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>368.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>467.000000</td>\n",
       "      <td>989.500000</td>\n",
       "      <td>1082.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1444.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1979.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>163000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2189.500000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>11570.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2001.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>733.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>805.500000</td>\n",
       "      <td>1302.000000</td>\n",
       "      <td>1387.500000</td>\n",
       "      <td>704.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1743.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>214000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2919.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>215245.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>5644.000000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>2336.000000</td>\n",
       "      <td>6110.000000</td>\n",
       "      <td>5095.000000</td>\n",
       "      <td>2065.000000</td>\n",
       "      <td>1064.000000</td>\n",
       "      <td>5642.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2207.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1488.000000</td>\n",
       "      <td>1424.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>1012.000000</td>\n",
       "      <td>508.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>17000.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>755000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Id   MSSubClass  LotFrontage        LotArea  OverallQual  \\\n",
       "count  2919.000000  2919.000000  2433.000000    2919.000000  2919.000000   \n",
       "mean   1460.000000    57.137718    69.305795   10168.114080     6.089072   \n",
       "std     842.787043    42.517628    23.344905    7886.996359     1.409947   \n",
       "min       1.000000    20.000000    21.000000    1300.000000     1.000000   \n",
       "25%     730.500000    20.000000    59.000000    7478.000000     5.000000   \n",
       "50%    1460.000000    50.000000    68.000000    9453.000000     6.000000   \n",
       "75%    2189.500000    70.000000    80.000000   11570.000000     7.000000   \n",
       "max    2919.000000   190.000000   313.000000  215245.000000    10.000000   \n",
       "\n",
       "       OverallCond    YearBuilt  YearRemodAdd   MasVnrArea   BsmtFinSF1  \\\n",
       "count  2919.000000  2919.000000   2919.000000  2896.000000  2918.000000   \n",
       "mean      5.564577  1971.312778   1984.264474   102.201312   441.423235   \n",
       "std       1.113131    30.291442     20.894344   179.334253   455.610826   \n",
       "min       1.000000  1872.000000   1950.000000     0.000000     0.000000   \n",
       "25%       5.000000  1953.500000   1965.000000     0.000000     0.000000   \n",
       "50%       5.000000  1973.000000   1993.000000     0.000000   368.500000   \n",
       "75%       6.000000  2001.000000   2004.000000   164.000000   733.000000   \n",
       "max       9.000000  2010.000000   2010.000000  1600.000000  5644.000000   \n",
       "\n",
       "        BsmtFinSF2    BsmtUnfSF  TotalBsmtSF     1stFlrSF     2ndFlrSF  \\\n",
       "count  2918.000000  2918.000000  2918.000000  2919.000000  2919.000000   \n",
       "mean     49.582248   560.772104  1051.777587  1159.581706   336.483727   \n",
       "std     169.205611   439.543659   440.766258   392.362079   428.701456   \n",
       "min       0.000000     0.000000     0.000000   334.000000     0.000000   \n",
       "25%       0.000000   220.000000   793.000000   876.000000     0.000000   \n",
       "50%       0.000000   467.000000   989.500000  1082.000000     0.000000   \n",
       "75%       0.000000   805.500000  1302.000000  1387.500000   704.000000   \n",
       "max    1526.000000  2336.000000  6110.000000  5095.000000  2065.000000   \n",
       "\n",
       "       LowQualFinSF    GrLivArea  BsmtFullBath  BsmtHalfBath     FullBath  \\\n",
       "count   2919.000000  2919.000000   2917.000000   2917.000000  2919.000000   \n",
       "mean       4.694416  1500.759849      0.429894      0.061364     1.568003   \n",
       "std       46.396825   506.051045      0.524736      0.245687     0.552969   \n",
       "min        0.000000   334.000000      0.000000      0.000000     0.000000   \n",
       "25%        0.000000  1126.000000      0.000000      0.000000     1.000000   \n",
       "50%        0.000000  1444.000000      0.000000      0.000000     2.000000   \n",
       "75%        0.000000  1743.500000      1.000000      0.000000     2.000000   \n",
       "max     1064.000000  5642.000000      3.000000      2.000000     4.000000   \n",
       "\n",
       "          HalfBath  BedroomAbvGr  KitchenAbvGr  TotRmsAbvGrd   Fireplaces  \\\n",
       "count  2919.000000   2919.000000   2919.000000   2919.000000  2919.000000   \n",
       "mean      0.380267      2.860226      1.044536      6.451524     0.597122   \n",
       "std       0.502872      0.822693      0.214462      1.569379     0.646129   \n",
       "min       0.000000      0.000000      0.000000      2.000000     0.000000   \n",
       "25%       0.000000      2.000000      1.000000      5.000000     0.000000   \n",
       "50%       0.000000      3.000000      1.000000      6.000000     1.000000   \n",
       "75%       1.000000      3.000000      1.000000      7.000000     1.000000   \n",
       "max       2.000000      8.000000      3.000000     15.000000     4.000000   \n",
       "\n",
       "       GarageYrBlt   GarageCars   GarageArea   WoodDeckSF  OpenPorchSF  \\\n",
       "count  2760.000000  2918.000000  2918.000000  2919.000000  2919.000000   \n",
       "mean   1978.113406     1.766621   472.874572    93.709832    47.486811   \n",
       "std      25.574285     0.761624   215.394815   126.526589    67.575493   \n",
       "min    1895.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%    1960.000000     1.000000   320.000000     0.000000     0.000000   \n",
       "50%    1979.000000     2.000000   480.000000     0.000000    26.000000   \n",
       "75%    2002.000000     2.000000   576.000000   168.000000    70.000000   \n",
       "max    2207.000000     5.000000  1488.000000  1424.000000   742.000000   \n",
       "\n",
       "       EnclosedPorch    3SsnPorch  ScreenPorch     PoolArea       MiscVal  \\\n",
       "count    2919.000000  2919.000000  2919.000000  2919.000000   2919.000000   \n",
       "mean       23.098321     2.602261    16.062350     2.251799     50.825968   \n",
       "std        64.244246    25.188169    56.184365    35.663946    567.402211   \n",
       "min         0.000000     0.000000     0.000000     0.000000      0.000000   \n",
       "25%         0.000000     0.000000     0.000000     0.000000      0.000000   \n",
       "50%         0.000000     0.000000     0.000000     0.000000      0.000000   \n",
       "75%         0.000000     0.000000     0.000000     0.000000      0.000000   \n",
       "max      1012.000000   508.000000   576.000000   800.000000  17000.000000   \n",
       "\n",
       "            MoSold       YrSold      SalePrice  \n",
       "count  2919.000000  2919.000000    1460.000000  \n",
       "mean      6.213087  2007.792737  180921.195890  \n",
       "std       2.714762     1.314964   79442.502883  \n",
       "min       1.000000  2006.000000   34900.000000  \n",
       "25%       4.000000  2007.000000  129975.000000  \n",
       "50%       6.000000  2008.000000  163000.000000  \n",
       "75%       8.000000  2009.000000  214000.000000  \n",
       "max      12.000000  2010.000000  755000.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 将nan值统一填充为 np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.fillna(np.nan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MSZoning 根据Neighborhood 填充缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = df.MSZoning[df.MSZoning.isnull()==True].index\n",
    "\n",
    "df.loc[index[:3], 'MSZoning'] = 'IDOTRR'\n",
    "df.loc[index[3:], 'MSZoning'] = 'Mitchel'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 将 YearBuilt, YearRemodAdd, GarageYrAdd, YrSold, MssubClass 类型转为 object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['YearBuilt'] = df['YearBuilt'].apply(str)\n",
    "df['YearRemodAdd'] = df['YearRemodAdd'].apply(str)\n",
    "df['GarageYrBlt'] = df['GarageYrBlt'].apply(str)\n",
    "df['YrSold'] = df['YrSold'].apply(str)\n",
    "df['MSSubClass'] = df['MSSubClass'].apply(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond',\n",
       "       'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF',\n",
       "       '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath',\n",
       "       'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr',\n",
       "       'TotRmsAbvGrd', 'Fireplaces', 'GarageCars', 'GarageArea', 'WoodDeckSF',\n",
       "       'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea',\n",
       "       'MiscVal', 'MoSold', 'SalePrice'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_cols = df.dtypes[df.dtypes != 'object'].index\n",
    "obj_cols = df.dtypes[df.dtypes == 'object'].index\n",
    "num_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GrLivArea 去除 4500 以上的两个离群点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['GrLivArea'] = df['GrLivArea'].apply(np.log1p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((array([-3.49454834, -3.25034572, -3.11527604, ...,  3.11527604,\n",
       "          3.25034572,  3.49454834]),\n",
       "  array([ 334,  407,  438, ..., 4676, 5095, 5642], dtype=int64)),\n",
       " (489.28523128867596, 1500.7598492634463, 0.9659918130329507))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['GrLivArea'].skew()\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "stats.probplot(df['GrLivArea'], plot=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(train['GrLivArea'][train['GrLivArea']>4500].index, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d1c84420f0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+RXYSqAshxqWUk2bLWThm0RMuPEDXWrPlnW7WbW5jyzvdqfFZlT7+9sQFnH/CfKoCcssWQoyfTF6dOuSuL4QYl1JOmi1H4ZiTQY/EC/uDl7BsNr7Ryf2b2nizcyA1ftisCi5paeJDR8/F55FOukKIiSOTV6cOCdSFEONWykmz5WK0AXooluCRV/by4JY2OvqjqfETmmq5pKWZkxfOwlBSKyqEmHilnDgrRkcCdSGEGIdQLEF3KE60wAC9ayDKQ9va2fDSe6mFjQwFZyyaw6qWZo6eV1PMyxVCiJJOnBWjU9RAXSn1NtAPWEBCa92ilJoFrAMOB94GLtFadyulFHAT8NdACPi01nqre5wrgG+5h/1XrfVP3fFlwF1AEHgU+Duttc51jmK+VzFzyOI+AmAwmqAnXHiA/k7XIA9sbuOJ1/cRt5wJon6PwTnHHsJFLU0sqAuO6vxKKSp9JjVDOu4IIUQhSjlxVhRuMjLqZ2mt96f9/g3gKa31DUqpb7i/XwOcCyxyv04GbgNOdoPu7wAtgAa2KKU2uIH3bcBVwAs4gfo5wG/ynEOIcZHFfcRgNEF3KEYsYY+4rdaaV9p7Wbepjed3d6XGa4NePvb++ax8/3zqKkb3UbPHMKgOeKgOePCYUrsuhBDTWSlKX1YCK9yffwpsxAmiVwJ3a6018IJSqk4pNc/d9gmt9QEApdQTwDlKqY1Ajdb6eXf8buBjOIF6rnMIMS6yuM/MNRBN0FNggG7Zmj+8uZ/7N7fy+nv9qfH5dQEuXtbMR46dS8A7uklbAa+TPa/0mSipXRdCiBmh2IG6Bn6rlNLAGq31WmCu1vo9AK31e0qpZHSzAGhN27fNHcs33pZlnDznEGJcZHGfmWc0AXokbvH4q3t5YEsbe3oiqfGj51WzqqWZ04+agzmKGlClFFV+DzVBD/5J6MYg5YpiOpIVOMVUVuxA/XSt9R43UH5CKbU9z7bZ/lejxzBeMKXUVTilMxx66KGj2VXMULK4z8wxEE3QPRgjbo0coPeG4vzPi+38z4t76A3HU+OnLpzNquVN/MWC2lFlwb2mQU3AS3XAU4qAQsoVxbQhK3CKqa6ogbrWeo/7vUMp9RBwErBPKTXPzXTPAzrczduA5rTdm4A97viKIeMb3fGmLNuT5xxDr28tsBagpaVlVEG+mJlm4uI+M23ybH8kTk8oXlCA3t4TZv3mNh57dS9RN+PuNRVnHz2Xi1uaOGx25ajOXeFzsufpD4JlQMoVxZQlK3CKqa5ofw2UUpWAobXud3/+MHAdsAG4ArjB/f6wu8sG4MtKqftwsjO9bqD9OPBvSql6d7sPA9/UWh9QSvUrpU4B/ghcDvxX2rGynUOIcZlpi/vMlMmzWmv6owl6CwzQX3+vj3WbW/nDzv3Y7iN+ld/D+SfM44ITFzC7qvAAwDSS5S1evKWfHFrW5YryKagYLVmBU0x1xUzbzAUecj/u9QD3aq0fU0ptAu5XSn0WeBe42N3+UZxaxzdx6h2vBHAD8uuBTe521yUzNcAXOVjv+Bv3C5wAPds5hBi3mbS4z3SfPDuaAN3Wmj/uPsC6za283NabGm+s9nPRsib++i8OGVUm3OcxqAl6qfZ7ymlyaFmXK8qnoGK0ZuIKnFKTP70ULVDXWu8GTsgy3gV8KMu4Br6U41g/AX6SZXwzcFyh5xBCjE6pJ88Wq+wmGaD3DMZJ2PkD9FjC5qnX93H/5jbeOXDwfR/VUMUly5tY8b6Ggtskpvc+H23Xl8lQ7uWKQozWTFuBU2ryp5+yKoQUQpSXUk6eLUbZjdaavoiTQR8pQB+IJNjw0h4e2tZO12AsNd5yWD2rljez9NC6gjPhHsOgJuihOuAdVdeXySTlimI6mmkrcEpN/vQjgboQlOeEyXK4plJOnp3IspvRBOj7+iI8uLWNR17eS9hdddQ0FGctbmBVSzNHNlYVfN6gz6Qm4KViavQ+l3JFMS3NpBU4y6EmX0pvJpYE6mLGK8cJk+VyTaWcPDsRZTdaa/rCCXrDIwfouzoGWLe5lae3d6QmiAa9JucdP48Lly6gsSZQ0DkNpagKeKgJePF5Sj45tGBSrijE1FfqmnwpvZl4EqiLGa8cJ0yW0zWVavLseMpubFvTH0nQE45h2bnnHGqt2fJON+s2t7HlnYPr68yu9PG3Sxfw0ePnUxUo7DbpNQ9ODpU/SELMPOWQSS51Tb6U3kw8CdTFjFfqCZPZlOM1TbaxlN3YtqYvEqc3HM8boCcsm41vdHL/pjbe7BxIjR82q4JLljfzoSWNBWfDK/1O9jzoK7/JoUJMV+UQFA+9nnLIJJe6Jr8cSm+mm4IDdaXUGcAirfWdSqkGoEpr/VbxLk2IyTHeCZPFqCWXFVBHV3Zj25recJy+SP4APRRL8Mgre3lwSxsd/dHU+AlNtaxa3sxJR8zCKKCW3DQU1e7KoWXQ+zwruWeL6apcguJ05ZRJLmVNfqlLb6ajggJ1pVRyOejFwJ2AF/gZcHrxLk2IyTGeCZPFqiXPd03lMMl0soxUdmPZmr6wk0G3de4AvWsgyi+3tfOrl95jIJoAwFDwl4saWLW8iSWH1BR0PX6vSU3AQ1V59T4fRu7ZYjorp6A4STLJjlKX3kxHhWbULwBOBLaC02tXKVVdtKsSYhKNZ8JksWrJc10TUBaTTEvNSmbQRwjQ3+ka5P7NbTz5+j7ilrOd32NwznGHcPGyJubXBUc8l1KKSr/TvaUce5/nIPdsMW2NNyguRtmMZJIdpS69mY4KDdRjWmvtLiud7LcrxJSSLxM91gmTxawlz3ZNl659YVwPBlM9G19IgK615pX2XtZtauP53V2p8dqglwtOnM/KExZQW+HNum+6qdD7PA+5Z4tpazxBcbHKZiSTfNBMaoc5GQoN1O9XSq0B6pRSnwc+A/y4eJclxMQqVonKZNeSJx8M+iNxOvujxCwbr6HoDcdH3LdcWj6OhWVrekIx+iOJnAG6ZWuee3M/6za38vp7/anx+XUBLl7WzEeOnVtQRjzZ+7zSP6Xn2ss9W0xb4wmKi1U2ky+TXG4TX8XUUtBfIq31D5RSZwN9ODWP39ZaP1HUKxNiAhWrRGWyFwRqrq/g7a4BugbiKOVMaoy7rQg3bu/I+17KqeVjoRKW7U4STaBzBOiRuMXjr+7lgS1t7OmJpMaPnlfNqpZmTj9qzogZ8ana+zwXuWeL6Ww85RXFrCXPlkkux4mvYmopdDLpEcDvkzd6pVRQKXW41vrtYl6cEBOlWCUqk70g0OozF7L6Z1vQaAwU2l3Dp77CO2LAPdH/BsUso0lYNj3hOP15AvSeUIz/eXEPD7+4J+MThdOOnM2qlmaOW1Az4oTP6dr7XO7ZYrrIlY0ea3nFZNeS58rg//Lq02isHnkRNcnGi0I/230AOC3td8sdWz7hVyREERRSojLWwDNZS57c/1sP/5nmZ4sTsK9Y0kiV3yQSt4lZNj7TYE5VgOqAh7buUN73MJFlOsUqoykkQG/vCbN+cxuPvbqXaMJ5UvGairOPmcsly5o5dPbI72cG9D6Xe7aY8oqRjZ7sWvJcGfxQ1MKu1Hnfh2TjBRQeqHu01rHkL1rrmFJq5s2QEFPWSCUq4w08J7P++31za4YF3KFYgkqfmfcaJrJMZ6LLaOKWTU8ozkA0d4D++nt9rNvcyu/f2E9yiyq/h5Xvn88FJy5g1gh/aJO9z2sCHjxl2vt8Ask9W0x5xagnn+yuJLky+G/tH6TS78n7Pib6/Ut2fmoq9K9Vp1Lq/OQvSqmVwP7iXJIQE2/FkkauO/9YGqsD9IbjNFYHuO78Y1NBZXrgORBNsLc3QntPiK/et42N2ztGPH76/ko5372mYs2zuyf8vaw+cyFxSxOKOUFtKJYgbmmUUnmvYaR/g9Fo7Q4RHDIxcyxlNHHLprM/Slt3mP5IfFiQbmvN87u6+D/rXuRL927jWTdIb6z2c/WKI7nvqpP57BlH5A3S/V6Thmo/h86qYFalbyYE6SD3bDENFKuePFk2s6C+goZqf1GD1dmVPtZctoymeqcVbFN9kBsvPJ6bn9o54vuYyPefzM5fcOtznH7jM1xw63Ps2NePnWeBOFEeCs2ofwH4uVLqR4ACWoHLi3ZVQhRBvhaM6d1U9vREUAo8hiIUswrKjBezTSMML8u5aOkCnt99IKMu/lsP/3nEaxhrG8qhxltGE7dsukMxBqNW1gx6LGHz1Ov7uH9zG+8cOHj9RzVUsWp5Ex94X0PegDvZ+7w26MU/w/oYu+SeLaa8QurJx5MlnowMs2Eo5tUFuH7lcVT4THrCcX7w+A46B6Kp95HrOiaynr4cF4kShSm068su4BSlVBWgtNb9I+0jxFSSDDw7+6Mo5XQBsW3we1QqK50vwC1mm8ZsZTXrt7YPy4Y3Pzt5rSKTZTSd/RH6IwmiCRvTUKw8YX7e/WIJm55wjIFIIuvrA5EEG17aw0Pb2ukaTFVu0HJYPauWN7P00Lq8E0S9pkFNwEtVwDMVe59PGLlni+lgpHry8dRwT2b9d13QxyG1gazvI991TGQ9vaycOnXlDdSVUpdprX+mlPqHIeMAaK3/o4jXJsSkSQaekYSFx3CCdBvNnKpAQZnxYrZpLLQefDJbRa5Y0shFbT3csnEXCdvGbxrUVnhZv7Wd45vqhj3UjBSg7+uLsH5LG4++spdw3PnDYRqKsxY3sKqlmSMbq/JeT4XPQ03Qk/GQMhPJPVtMJyPVk4+no8pkZpjzvY/O/mje65ioenpZOXXqGumvWnI1uzEvPa2UMoHNQLvW+jy3bdh9wCyc5a0/5U508gN3A8uALmBVspWYUuqbwGdxOhd8VWv9uDt+DnATYAJ3aK1vcMeznmOs70FMP9m6o1x3/rF89b5thGIWfo9iTlWAmqCXUCwxYla6kDaNY+0qU0hZTfLYg9E4cUvj8xgsaqwuaqvI53cfoKk+OGxSa/oDRCxh0xOKMRDNHqC/2THAuk2tPLOjg2SpZNBrct7x87hw6QIaa3L/sTWUojrgrBw6HXqfT5Bx37OFKCf52jCOp6NKsTPM2cpZsr2Pka5jolb5lJVTp668gbrWeo0baPdprX84xnP8HfA6UOP+fiPwQ631fUqp23EC8Nvc791a66OUUh93t1ullDoG+DhwLDAfeFIp9T73WLcAZwNtwCal1Aat9Wt5ziFE7g4t5x/LzR8/MfVa0GumJmoWkpXOV/89nq4wI5XVpB97Xm0wI5NezIWM8j1ARBMWvW4Xl6G01mx5p5t1m9vY8k53anx2pY8Lly7gvOPnUxXIfWvyeZze51W+6dX7fCJM0D1biClhPB1ViplhHk1ZzWRlupNZ/Q1fPp1wzMLSuqCVmkXpjZiG0lpbwPkjbZeNUqoJ+BvgDvd3BXwQWO9u8lPgY+7PK93fcV//kLv9SuA+rXVUa/0W8CZwkvv1ptZ6t5stvw9YOcI5xBSxcXsHl659gTNufJpL175QUOeVbPts3N7Buf/5LIu/9RsW/fOjHH3tb/jc3Zvo6I+QcDulpHdHmcjOKOnG0xUmV5eX5MPDZHacSddcX5EqU0kKxRI0Vgdo7w4PC9ITls2Tr+/jqnu28PUHX0kF6YfNruBrH1nMzz93Mh8/6dCsQbpSiiq/h/l1QZrqK6gJeCVIz2E892whppLxdFRJZpjT952oDHOuspr0eTeTcR3Z7OuLsmrtC5z5vY387a3/K51fpoBCCzr/1+0esA4YTA5qrbeOsN9/Al/n4Mews4EerXXyL3gbsMD9eQFOZwK01gmlVK+7/QLghbRjpu/TOmT85BHOkUEpdRVwFcChhx46wlsRozGeVSvHkn3Ots8/rX+JuGUzGLUATcKGuOXckLS22dPr3ERrgt6MUpKJ6oySbjxdYUYqqyl2x5lc0mvi/R6DwWiCmKW5allTxnahWIJHXtnLg1va6OiPpsZPaKpl1fJmTjpiFkaOCaIew3DLW2ZE7/OJNNZ7tpQrirKVrZxkpI4quRTST32sXWEKKatJP/bcGj+/vPo04gm7qP3NpfPL1FRooJ5c4e66tDGNk7nOSil1HtChtd6ilFqRHM6yqR7htVzj2f5q59t++KDWa4G1AC0tLfJIOUHGu/jPWBbTufGx7XQmukbvAAAgAElEQVT0RbC0xmcaNFT7GYgmiMZtfB7D6TPOwf8QbMCDYv9AlJqgl3DcotJncunaF8b0cJHr3yH5sNIXjmPZNnOqDtZcj6YjS76Hh2J2nBnpmq61bG7/3W7ae0IcUhPk48ubOWnhLAD2D0T55dZ2fvXyHvdhCQwFf7mogVXLm1hySE3OYwe8JjVBL5U+M2+XF5HTqO/ZaaRcUZSdXOUkixqqcnZUGUm++u/xdIUZqZylVCuOSueXqanQ9oxnjeHYpwPnK6X+Ggjg3PT/E6hTSnncjHcTsMfdvg1oBtqUUh6gFjiQNp6Uvk+28f15ziEmwXhXrRxthnjj9g7e6BjAVE6nkISt2dPjBO0aUAp08hHOjdS1Bo0m5paU9IbjKCBu6wlZWXTow0rCsunodxKEsyv9Y+rIkutTisns9pIUiVv0hOIc2VjF9y8+PuO1t7sGeWBzG0+8to+E+5Gq32NwznGHcNGyJhbUBbMeM1neUhP0zNTe5xNmjPfs9HLF7wL/kFZK+Al3k58C/4ITRK90fwan1PBHQ8sVgbeUUslyRXDLFd1zJcsVX89zDjHNZcta27amYyBK3LLxGAq/xyBmaX74xI6s2eBirDQ6nuzzSBM3S5XZls4vU9NI7RlPxsk4Hwm8AnxGa/16IQfWWn8T+KZ7nBXAP2mtP6mUegC4COdjziuAh91dNri/P+++/rTWWiulNgD3KqX+Ayc7swj4E07Ytcj9yLQdJ4PzCXefZ3KcQ0yC8ZZi5MsQZwtWkw8G2gaFQimntaK2nSx6NG478bkbpCvAY0DC1tgaOvuj1AQ8+L3mmB8uhhr6sNLgtgobjFp4jHjWrjDZJN/vzo5++iMJ6iu8zKnyD3uQGKnjzERJBuihWGb9udaal9t7WbeplRd2H0iN1wW9fOzE+aw8YQG1FQf/m/jT7gPct6mV9/rCzK8N8rkzjuDcv5gndefjNJ57tqusyxVF8Yx38Z+x7J8ts/yLz59MbzjBF362JTV26yeX8shL7Vxx2hF09sfY1toDHMwGT1RnlHTjyT6PVFZTqsy2dH6ZmkbKqN8C/BPwLM7kpP8EPjLOc14D3KeU+ldgG/Df7vh/A/e42ZcDOIE3WutXlVL3A68BCeBL7mQplFJfBh7HqXf8idb61RHOISbBeEsxcmWIT104KyNL/XbXAKt/toVYwi1vsW28GG4G3QnC08tdkjSkXvObisFYgr5IgoYqb8Y1j6fOO9vDypwqP73hOL+/ppDqg8ysfCiawNbOH0K/x0y1jUw+SBSjrj5drgDdsjV/eHM/6za1sn3vwTV1FtQFubiliY8cMxf/kM4Cf9p9gJue3onPVMyu8NEXiXPj4zuo9HuK+h5miDHfs6dCuaLMKyqO8ZZijHX/bJnlaEKngvTk2NU/38qdn17OlXdt4trzjmH1PVuAic8Gpz9sKKXGlX3O9/BQqsx2IXX5ovyMFKgbWusn3J8fcCcIjZrWeiOw0f15Nwc/Bk3fJgJcnGP/7+J8FDt0/FHg0SzjWc8hJsd4SzGyZYhPXTiLO/7wFoOxBF7DwNI6NTFUAbbWbi2zxtJOCUzQUNQGvXSH4sQSdkZ0kSyRiVk6Nd45EMdQkVTv7vHUeU9E3Xh6Vj5ua0zD+dQgWVc/GRNGI3GL7lCMcMwaNv74q3t5YEsbe3oiqfFj5lVzyfJmTj9yTtaVQU1DsX5rG0GvQaXfeZCpMI1xfXohMoznnl325Yoyr6g4xluKMdb9s2WWDUXWbLNpKNq6w6nsb1N9kLs/cxIaTXt3aNxB59CHjQ8f08jtly3LyOyPJfuc7ZOGUma2i/HpgyiukQL1OqXU3+b6XWv9y+JclpjKJqIUIz1DfPOTb3DLxl1EEzYAlm1nbKtxboaGofCYBofUBlLtDOdU+VNlJ+Bk2l/f2+8G98PPu68/SsBr4DGNcdV5T0TdeHpW3mcaJGyNUhCznPdfzAmjuQL0nlCM/3lxDw+/uIfecDw1ftqRs7mkpYm/WFCbdfJnsvd5td/D3r5ISbrUzBBjvmdLueLMNd5SjLHsb9say9bDMsu2Jmu2Obnt/Logz11zFkGfyb6+KJff+r8TMiFz6MPGb19zWgLfv/pUtNajKudJBuZej8FAJMHlP/nTsGucapnt8ZZGibEbKVD/HfDRHL9rQAJ1kdVElWJs3N7BLRt3YWuNobIH1wrwmgaGgkjCprE6kKpdz5bVrvSZ9OVYyh5gb1+UpYfWj6vOeyIeVtKz8g3Vfvb0RLBxutqMZiGm0QjHnAA9MqQ/ent3mAe2tPHYq3uJuQ9MXlNx9tFzubilicNmVw47llKKSp9TppO+sEaputTMEMW4Z0u54jQ33lKMfPvnCvC6BmP86yOvceOFx3PNgy+nAlmfR3HbZcv44pAa9ed2dnDv505OBc3AhE7IzPaw8dvXOvjORzULCrw3ZSsB+v5Fx9NQ5aetOzzsGqdKZrtUXWqEY6SVSa+crAsRIps1z+4mYdt4TQO0U+IylMcASzs308bqAL+46pTUa9/e8Cr7ByL0huJELRuPYfDXx83loRffy3nOxmp/xjHGarQPK0Mnyp66cBbrt7YTiiWo8nuYXeXlwGCcoNdIPYxMVKlIrgD99ff6WLepld/v3J8qEQp4DWoDXiztdNfZ1xvNCNRH6n1eii41M8VE3bOlXHFmGW8pRq79vaZzD1mdFnSv+dQy5lT6SNiazv4YP3h8B9eedwx1QS9xyyaWsPn1i23c+enleEyFzzQI+gxMYw6fuOOPqePcftmyVACcNJ4JmRNRN56tBOhr61/OqKufiu0Qpf96aRXUnlEpNRf4N2C+1vpct0/uqVpryXqIomrtDuE3DSyNU45iZ97gPIaTuTUMNSzYW7GkkYvaerhl4y4sWxPwmFQHPGx5t5c5lV72D8aHng6fqUqS2c3We3791nYuWrqA53cfoK07xOGzq/j/L5jYbi6hWILuUJxoWoBua80Lu7tYt6mNV9p7U+Nza/wsP2wWm945gM80CHgNugaj3PT0Tv6ORZy5uKGg3ueT2aVmppJ7thiN8U4yHLp/suRj27u9XPvwnzMCvNX3bOHOTy/n+49v5+vnLOZ7j+1IBbF3fno5n75zE23dYdb8/m3ACZbvX30qq+/JnGD6hZ9t4fqVx3HlXZtS1zGeCZkTUTeeqwQovdRvKrZDlP7rpVXogkd3AXcC/+z+/gbOindy0xdF1VxfgWXbdA3E3bIPRcxdvKihykdfJEHctlk4q5JvnHv0sGDv+d0HaKoPZpRZJGvXYwmbvujBG42poDboLWpmN1cv9Fy955/ffWBCsvtDDUYT9IQzA/RYwubJ1/fxwOY23jlwsF78qIYqVi1vYsXiRr72wMtOhsstZQn6TKIJm4debOfjJxfehaPYXWqE3LPF6Ix3kmH6/gcGo7zTFeKw2RVZA7zecJwrTjuCO597i69+aBFX3rWJpvogR8ypzLp93LKzjh86uyKVBR/vhMyJ6IiSKysfcuf6TNV2iNJ/vbQKDdTnaK3vT3YQcHvmyqOUKLpkmcTsKpzyFVvjNRVzq/0opTixsTpvNjZXT/fecJybL13KDb95nbe6nKB04ZxKrjlnCUDOFUpzBdrZ5CplybZi63h7zxdqIJqgJxRL1ZkD9Efi/Oql9/jltnYODB5ctb3lsHpWLW9m6aF1qQz5e31hagIelFKYhsJQzkTXPT3hYecSJSX3bFEStq15ryfCtQ//mWvPOyZrgNc1GOP6X7/Gtecdw5GNVTx3zVn4PCaa4ZNLm+qDeE0j6/h7PWGuPe8YlhxSTYXPM+4JjmN5WBk6efTuz5w0bPLo3Bp/6j1OxUmY0n+9tAoN1AeVUrNxe9sqpU4BevPvIsT4ZZRJGKMvk8g3cTFbVjdbCUoymAZyvrZiSWNGYF7lM+kajFET9Ka2vWXjLuorvNQGnS406YsqFTrBcjQPCumyBej7+iI8uLWNR17eS9jNrJuG4qzFDaxqaebIxqphx1lQF6Q7FKPKf7D2PBRLyETQ8iP3bFESXYOxVE367Rt3DZsseuOFx/ODx3ekWi0GvWYqOLZtPSwgXPOpZTRU+lhz2bKMWvfkcToHoty/+tSS1ErnmmS54cunE44NycoPn28/qdc5no4t0n+9tJTOMjlv2EZKLQX+CzgO+DPQAFyktX65uJc3eVpaWvTmzZtLfRligqUH3ukTF687/9iMADcZAG99txsFHFIboDrgZLhDsQSNbovHocF08rVk5j95njc7B0hYTnYInNVPB9NaHSqcevhDagPYGq5feVxq/4Rls68vSty2WdRQlSrpKfS9pBuIJugejBG3Dgbob3YMsG5TK8/s6Eh10Ql6Tc47fh4XLl2Q6iOfZBqK6oCX6oCH53buH/U1iOJTSm3RWrek/S73bFES7d0hTr/xmdTvJzbX8YUVR7KosYqdHQPcvnEX21p7aKoPcu/nTqapviIj4EskbPb0hunoj9I1GOPBLa38/dmLaazxsXPfIA3Vft7tCnHzUzvpHIhy+2XLWDK3Go8n23pa45cvyO3sj3LBrc8Ny/SX0yRL6dhSnobes/NuW0ig7h7UAyzGiTF2aK2Hz8SbwuSmX97GmklO3zfXxMX0APjdAyF3mUTF/DonWNda0xuOo4G6oDdjomTytaa0jHhfOM67B0LOUowKDHchpkRm+3fAWarxfXOreOzvP8DG7R3c+Nh23ugYwEBjGAaWu9DRl1YcyfO7D+R8UBhax94fidMTiqcCdK01m9/p5v5NrWx5tye13ewqHxeeuIDzjp9PVSDzAza/16Qm4KHK78l4zyP9e4rJl+2mL/dsUQq5gtd7P3dyRteWNZ9axuLG4QF2rv03fPl09vVF+eETO7hwWTOzK300VPuZXxPANI2cwfR4sskjBblDH0qSnrvmrIJbOhbbVHiYmIlGE6jnLX0ZsnBGuvcppWTBIzEp8pWjFFofni+YTJ/I6TMNEpYG5dzgqgNe9g9ECcUsogmb/QNR5lYHqHHryZPlKcka875wnD29mYt32FpnXSMdwAbe7Q5zxo1P01xfgdaaar9JbziBtm2nd7yluWXjLqr8JvNqgxn7p9exa63dEpeDAXrCsnlmRyf3b25lV+dgar/DZ1dwSUszHzq60Wl96VJKUek3qQlk9j5PJxNBy5fcs0Wp5apnnl8bLKh0IleHkXDMYvHcar57wfEZxwByBtP5Xst27qFBvWnk79U+FSZZSseWqW+kGvWP5nlNFjwSk2LNs7uJWxZdAwlilo3PNKgJenIuOT/awD59IuecKj97esMoDdGETWd/hM6BGI3VPuorvLT3RGjvCQM6Y/XS5OJK+weiGCg8BsTTVmfK97lVNGGlrnN352DGtslDxBI2cY9BOG4Nq2NfUBekLxKnNy1AD8USPPLKXh7c0kZHfzS1/QlNtaxa3szJR8zKyJIne5/XBL2Y8nHoVCb3bFFS+eqZC8ng5gt+sx2jsz+aM5iG/IF2emCeXOk0oz5+hF7t2R5K1nxqGfVpjQFKvaLnVHiYEPnJgkeiLKVnxPf1RbAsjddjYBqKhK3Z3x8jbvVn3Se9zlwplTFpM1ugnj6RM5kp39cfQWlFXySBqWD/QAyfaTC70kdvOE57T9jJwHsM1jy7O9XRJZqwMQ3IVVGWvD2rtFVWbRve2u/UXirl7OsUy2TyeZwHg+RCQaFYglhCc+z8Gi6/40+81xdmTqWz2t2mdw4w6LaeNBT85aIGVi1vYskhNRnHDHjNgnqfi6lB7tmiHIyn1WOhHUaSAXAolsibMc712tCyljs/vXxYz/ebnnqDmy89kZhlYwB7+yLc+dxbqSDXMBSLGqq493Mnp2rqb3ryDf7+7MVjyugXg3RsmfoK7fqCUupvgGOB1EwzrfV1ufcQYmyGZsTbu8NonAy3wglylSKjg0n6PslSkz09EebXQXXAm7fV4dCVMj2morE6wEVLF3DT029iKlIPCN2hOLVBkwMhm4ZqP0GvmbE40R1/eIvBaAJbg9dUKCBmOSG3kRacp1dlek3n2Ht6IqmxoUG6BuZU+vjGuUdz++920dodorE6wPubann0z3vRWhOOWbza1wfuoqt+j8E5xx3CxcuamF93sGRGKUWV30NN0INfsirTltyzRSmNNZNcSIeR9CA7VwvIZDCd67Whq23WVXgztjuxuY4rTjuCS3/8QirA/f5Fx/P1c5ZkZMy7w3E+cccfaajy84UVR/LZMxaytzfC3Bo/lp0/oz8ZpGPL1FfoyqS3AxXAWcAdwEXAn4p4XWIGG7r4TzqNm63W0BdJsHF7BwBfvW8boZiFx1AkLI0GFJp3D4QwDYWpFEfMyd4fK9dKmcnr0DYolJMFR9M1GMdnGlkXJ7r54yey+mdbsLXGVAqtwTTAwLkmr2kQTdhYGZG4wlAKO0+BTPKWeuJh9XzvohNI2DZaa1bfs5XuUIxI/OBDi6GgsTrAbZ9cSm3FwT8oXtOgJuClKuCZkPKW8UzwFcUl92xRSmPtNDI0uJ9XG8y6fXqQna0F5N2fOQmNJp6wufdzJ/Ovj7zGb1/ryMgmv9cbTgXQJzbXURv0ZgT1X1hxZOqY4ATZX1v/Mj+4+AQqfB601vg8JrZtc9rC2XxhxZEcGIylOtU0Vr+POVW+sqgPH+9iVqK0Cs2on6a1Pl4p9bLW+v8qpf4dqXUURbBxewdb3+3Gsm38HhOvaeSt7/7KL7aiILXCaGJIXbjWYGhNQkPnQJSbn3yD53cfGBZcDp0gmbyOuGU7wbZWeAyF1hpbw9yazJteMmO/YkkjVX6TSNx2Pi5VCg9go1Aa/KbCslXGdcYsG69WqbIXp1MMGV1iDGBff5SugSiWrbnrubd5aFs7obSVRb2mor7CR3XAZDBqpYJ0p6THk/XBZ6zGMsFXTCq5Z4uSGZqtLiSTPJrgPn2C5LbWHn7w+A6uPe8Yjj6kmqqAh319US6/9X8z6savX3kcXo9Bwta81xtGKcWHj2nkt6918IUVR3LDb17PCPhnV2YPshuq/Vyy5vnUse+6cjmXnXpYxiJHN154PDc99Qb/cv5xUh8uxq3Qv9zJ/8pCSqn5wAHgiOJckpipksGfclsaJmzNYCx/R7n+6MiZCVvDnEov3aEY//HkTrwGGIbB3t4IW9/t5ksrjuSrf/W+Yddh206QrnEeAGyt8XkMKgwDj5nZUix9caL3za2hoz9CwtLs6Q1joFBo4ramL2rhMTLLYMCZeGooWFDrpyrgZff+QZIFMB4FKKfl4k1P7uT3b+7PWEEUwFTQUOWjyu8lHLeYVxukNuilJujN6OoyUYZ+6jHSPAAx6eSeLSZVejYcyDsJM9s+SqmCg/uhEyS3tfZw/a9f46GrTydh62HHWX3PllR7x/QHgdsvWwY4bXd/+1oHnf0xrj3vGOqC3mEZdnCC7He7QhnHbj0QHlbbfs2DL3Pnp5djKrLWh9cHvXT2R6UURRSk0L/gv1ZK1QHfA7YAbwH3Fe2qxIyUDP7mVgecELWwFv95+UynrKQnnEhlqOO2U++esDXRhM1/PfNmqoQmeR2xhIVGDevAUuX38IUPLExN6tTa+Z7s/gJOzXvc0s6EVCD5f5JzNRN2ZpCeZChF1NKce9whAHgM5/oBLNv5evilPakgvdLn3OBNt2b/wGCMSMJCa/jqB49idpW/KEE6OJ1ygkPaN+abByAmndyzxaRJZsMvuPU5Tr/xGVatfYGvn7OYE5vrUts01QczJqwP3WdPT7jgMpHkBMnkgnLpAXAomru949AA/gs/28K/nH8czfVBmuqDbGvtYfU9W1i19gW+//h2bvnE0oxz3H7ZMm5+amfGsSt8Ztbz9Ybj7B+MsaihioeuPp3nrjmLh64+nUUNVezsHEi97wtufY4d+/qxs/1REIKR+6gvB1q11te7v1cBrwDbgR+OsG8AeBbwu+dZr7X+jlLqCJw/GLOArcCntNYxpZQfuBtYBnQBq7TWb7vH+ibwWcACvqq1ftwdPwe4CTCBO7TWN7jjWc8xin8XUQLJNonK59zM9w9EKSBhnpdSCtu2MQ2VNe5XQNzS3PjY9lQmuLU7RH8kgakUhqlI2HYqsG6o8qey73f84S0GYxaVPpPPnXFEav9kzXuyVt1vGlT5PRltEod2dTEULKhzutRsfGM/HgNCccj2tBL0GjRW+1MTQf0eg+5QjHDcpicUx+cxWPv7t1BKFS27nd4pJyn9UwVRGuO5ZwsxVtlKXZL13L3hOLMrfcyq9OExnHaKsyt99IRj7O2N8O8Xn0BPOE4kbhVcJpJrgmTXYIy39g9mPY6lddaAWmtN0Gfy/YuO52vrD9a5f/mDizikNsD9q08lYduYSrGnJ0LnQDTjGKFY9uvuGoxx/brXhn0ikK+dZLHryEvdKlKMzUjptjVADEApdSZwgzvWC6wdYd8o8EGt9QnA+4FzlFKnADcCP9RaLwK6cQJw3O/dWuujcP6g3Oie9xjg4zjdC84BblVKmUopE7gFOBc4BrjU3ZY85xBlrLm+grBbc10T9LKwoWrcx7TcCFvnWHQoGQY7pSYHryOasFFutxe/x8TvMajwGgxEnQms67e201Dt5+hDqmmo9rN+a3tGVn7FkkaWHlrPYbMrWdhQxUA0kTGBMz38NhUsqAvi95p4TcWbHf2E49kD9GPn1TCvJsCenjBvdAzwRscA+/sj+D0Gfo9BQ7WfQ2oCqZrx9GuaSMlPDXJ9qiBKZjz3bCFGzbY14Xj2Folza/xc/+vXuOj257n8J3+ioz/GPz/0Mm93DWaU7/lMg+ZZQW775PAMdl3AQ2d/lPbuEJ390VTmOTlBckF9RSrAjSUs5lT5hmXC11y2jIDXTI0lJR8EwjGL7z3m1Lk/dPVp3Pnp5SggkbCJxC0+8WNnRdV/e9SpY08/dvOs4LDs/o0XHs/tG3dlfCJg25rO/uiI7SSLZegnGJLJnzpGCtRNrfUB9+dVwFqt9YNa62uBo/LtqB0D7q9e90sDHwTWu+M/BT7m/rzS/R339Q8p53OylcB9Wuuo1vot4E3gJPfrTa31bjdbfh+w0t0n1zlEGVt95kJ6w3F2dvSzfW8fOzv6R94pj6ZaP4saq/CaBoahqPZnn8BjDongV5+5ENNQWFqjcWrTtYbaCi9N9RUZ9dnJPu1eU7Hm2d3DjpMMZqMJC8MN/D0GGQ8Nh9QGCHgMBiIJ3j0Qpj9qZQbyhnONCVtz0uH1dA5GSWvyQsyGnnCCSp854jVNlBVLGrnu/GNprA7QG47TWB3guvOPHVMGf+P2Di5d+wJn3Pg0l659oWgPFzPEmO/ZQoxWMvjb1TGYNQh+e39oWKnJ5acejmmoVBnMDb/ZzrUP/xnLhv96eifXnncM6646hWvPO4ZfvdjGGx1OmciX793Gn9t7efdAiI7+SEaAmbyO//urV9nTG8FjKn7x+VP41VfO4PqVxzGvLsCcSn/WcpnZlT58HpPOgSi3b9xFKGZx5V2b+OiPnuOStS+wry9CQ5WfnnCczoEoD29r585PL+fpf/wAd115EnOqfCyeW839q09l/RdO5drzjuEHj+9gW2sPTfVBvB4jI0jevrc/5wNDMeWa4Ns1KMUG5W6kyaSmUsqjtU4AHwKuGsW+uFnvLTh/IG4BdgE97vEA2oAF7s8LgFYArXVCKdULzHbHX0g7bPo+rUPGT3b3yXUOUeYUzk03bjlf+VT7TQZjTgBs2Qez1B4D6ip8/OGbfwUcnBza0R/JehxLw6LZB0s2Vixp5EsrjuSWjbuIxp3MugIODMa5dPks7t/SllrJNClbfXZm28cwSsG86gDVAQ+WrekciNITihGKWXT0RzPer99jUOEzCccSJGyNx1RU+D28trefeELjNRWWrVMLK2kgEs/MyBS7Znxop5yxkO4xE25c92whRiMZ/DVU+Ye1SLztk0v59sOvZmzf1h1mfl2QT97xx4wOKT94fAcHBmP89rUOfvta2nyhTy1j9c+20FDl558+sjjj+OkdYboGY/zwiR1ccdoRGdvc+smlHDa7grqgL28/8WTN+97eSNaWjNeedwy3b9zFjz5xImE3kE+/jpqAj4DXIG7ZXP/r1zL6rnvc68vXTnIyFiBK75ST/v+PyW4VKUZvpBv3L4DfKaX243QR+D2AUuoonI9S89JaW8D73UlNDwFHZ9vM/Z6rMiHXeLZPA/JtP4xS6ircP2SHHnpotk3EJEhfUVRrjWVrPIazcFGuT+VMA5rqKxiIOh8japw674YqP1UBD43VqTVeMmrGTeUE5ska8eR/LN84N/M/zWQd+i0bd5GwbfymQW2Fl/Vb26nymYTjFv3uZCHbbak4vzbAUMlgduP2Dq59+M8o5UxkDUUTRONOV5nu0MHONtV+k8bqANGE5WbHA5iGIhJPMLcmSGt3iIRt4zENPIbzPwGtnUmxUcvOOHeyZryc+51L95gJN+Z7tswrEqOVDP7ausOpFol1QS+NNX729g6v526qD/LOkK4p1zzoBMJdg7Fhtd7JFonXnnfMsAA6va47lrC4cFnzsG2u/vlWfnn1aSPWYSeD+Ep/9omhdUEv21p7GIgk+MYvX8l6Hbbt3H/v+cxJWFqztzfC9x7bwY8+cWJqWxjeTjLo80xKrfjQTjkgrSKnirylL1rr7wL/CNwFnKF1amF0A/hKoSfRWvcAG4FTgDqlVPIBoQnY4/7cBjQDuK/X4rQUS40P2SfX+P485xh6XWu11i1a65aGhoZC346YQMmM6ttdA0QTNjFLY+lkO8Th2xsK5lb7aaoL8mbHAO09zk3HVKQyF33h+LBa6WTN+OFzKjl8dgUVPqce3Gsqgj6Tbz3852FlF8/vPkBTfZBj5tVyZGM1c6oCzmqjStHZF6FjIJa6RlvDnt4INz/5Rup9Jcs5Pr7mefrCcb501lFUeD2094R5ry9CfzTB0A8N+qMW1QETjdMeC/EAACAASURBVCJu23hNRTRhkbCdUprm+go8hpHKpIPTe91jgMcwMmrG+8JxWg8M8tm7N7OttRtTUfTa9dGS7jETa5z3bJlXJEYlGfwBqY4p//jASxhKcedzbw2r577tk0u5+amdnNhcx5pPLUuVuMyvDfDgllZuv2xZxvazKn001QepC3rzZoOT2fFs28Tddl8j1WgbhiLo9WQtSwnFnPMEvNkD+VjCYv9gjK+tf5mz/v13fPrOTSilaKh2ymqCPpP7rjqFJ//hTJ75xw/w9XMW8+CWVqoCTpjyXm84o/6+GHJ1yil2Jl+M34gfhWqtX8gy9sZI+ymlGoC41rpHKRUE/grnZvwMzip59wFXAA+7u2xwf3/eff1prbVWSm0A7lVK/QcwH1iEs8KeAha5mZh2nD8Mn3D3yXUOUQLJjO7Ojn5iCSf4bKjyo5Ri9/7B1CJCQzuhDKVwJnoCtPeEsWynBMR2A3vLjXoH01rFpGeTsW32DcTQ2iktqfKb9EUsZgU8WcsuWrtDmAp2dw4Qs2x8psGcKh+xhJ1q9ahwWiN6DGft0Tv+8BbHN9Xx7Q2v4jGgymfS3hPmukdeo6kuyPZ9fTk/JUh6ub2Pn1yxfNhKqckM89fWv0R3KI5WzoFs7ZT6XH7KYTy/+wBt3SGq/B40sH8ghqlA286DxPzaYKp2vRwy1tI9ZuKN9Z7tBvW55hV9wh3/KfAvwG0484f+xR1fD/xo6Lwi4C2lVHJeEbjzigCUUsl5Ra/nOYcoY7Mrfdz9mZN4pytEhc8kFLNonhVkw7Z2vv3RYzEU3HfVKcQTNu09YSJxm4ZqX9YSla+fs4SGah+/vPo0InGbXR0DPPLSHm775DL2D0TzZoNnV/oIxxJ5tylkEaZkMJve9/zfLz4Bn0fx5D+ciT9HVlopxep7tgz7pODez51MfdDLzo4B/umBlzJKYr513jHs643y+XtGt3rrWOUr/RHlrZg1i/OAn7pZFAO4X2v9a6XUa8B9Sql/BbYB/+1u/9/APe5N/QBO4I3W+lWl1P3Aa0AC+JJbUoNS6svA4zgfo/5Ea50siLsmxznEJEtmzOOWRW8oDgoGY5ruwTjGCOUtQzmBZxTLTRKahnJuMrbzqgJ8HkXUsvj2hle5qK2H9Vvb8ZoKU0F7fwytnbKaSMIiFIPaoIeA1+St/YPELBu05up7tzKr0kdnf5SoG5EbCmLYtPdEWNRYxZ7eCH6PwlAHP5SytWYwZnHb73alOsb0RxJ0h+KE4hb7B2KpY1UHPPSGE2Rja6ckJFuJyooljXz/ohO48bHtqU41R82p4BvnHs2KJY181d3u0rUvELNsesNxZ+IWzr/T/oEoR8ypLJuM9eozF/LtDa8SiiUIep2SIukeUzoyr0iMVjRhpxb8SXZY+cCSRroHY3zx51szgtNDav1862+O4RNujTqklah88TRqg07AbNuawWgCqOO/nn6DK08/gts+uTTjeGsuW5bKBhuGYn6tswLpTU++wYXLmpld6aOx2k+9O5+okBrt9GA2HLfY1THADb/ZzrbWHgA+fEyjUzfvBuXJ4NpUZD22aSi6w/FUMJ4c/9r6l7nrypOGjRe7TWOyU854SZvHyVW0QF1r/TJwYpbx3RzMrqSPR4CLcxzru8B3s4w/Cjxa6DnE5EvWIHcNJDAMZ/GhZAvGsXzMN+h+BJlcSMiK61QWXruvBDwGXlNx++92Y+PUvFu2xlDKre1WzKv283ZXiJ5wgt5wAtMA23Zi/njMIm5FMiZ32hps9/cd+/oxFdhakX5vsmxN0Gvy1v4BlFLs7Y2nAn1wSnPm1QbQWlPh99AbHiCXfJMqC5nEmfw0wHIn5hoKTKWIWbqsMtaZE26Hf3ogJpfMKxKjkS1LvfpnW7jrypMYjMZTq5Mmg9NfXn0asYSdvUQlbY5Nes14coLpic11GTXwtUFvRnDo8Rgsbqzm7/7qfcMC6cVzqwuu0U4Gs8mHhWSdfVN9kL8/ezFHzq5g3VWnOJP8DUWF3yBukfPYuR4QjBzBfblP7kyWEA1dbbVYnwQI6QIgiiy5iFHMslFAJGGPuM9IUiUyWf6UJyybebUVJCybUNzCZypM9f/YO/f4KOp7779/M3vPbkgICShBBUQx0lAIl6B9lNY+VivK0wJeQcELoLb2WLXaKrWV+jwiWltaFfBYUBEUoR49tuqx2JRzKogGClUEUy6aIJA72c3eZ37PH7Mz7GY3ISAgl/m8XgjMzM7M7uLkM5/5fD8fQUIaMYtJXRID2pv2K8oS6HhaXSXO6NJsFpWAjtQlybTNm9oTVn47GJ7rPLdK3x5e9gajFPhcOBSBQxEkc9ysKOLLD1UG3A5q6kMoQiBT8ZIJKXGp4phTrA9HeoyNw4uUZbGKtLmilOKda66orptzRXSy3JorynGMjue1kFQe/IgRI+wA6K8Iui5pbI8RiWtWIoqpOte1RGgNx0loOj+//By+99Qaa7nUJbouu02YTc94XUvE8sCXFnpZevNoCrzZ3uqWSCLLgmKq1IVeJ4unjaS2OZJh0zEV91wqcUerSIHHwdb6EDOX7L8RmDux3CKrHcmrWcKU6/3qMje5F0KwqyWMEMaTYEVRjinFujsWIhuHF0emX9yGjRTMEiNTzT0cP1m7ulw5VYWAx8nethiKMJpJc5HhwwUh95N0AYRiWgZJD7hVevlduBwK/2tQL8JxjZr6IDtStpWOGe4CrGIkr1OlZm/bIWWMmzOEqjBuCEyoijjkvHMbJzaEEMUpJZ20uaJP2D9XBLnniiBtrii1/GohhDs1Q2TOFX1Aaq5ICOHCsDe+nnpNZ8ew8RXALOfpWDJkrtu6N8j3n3qPC+dWMfuNzdz9nbMZ1q8A2N/Kec+KTfTye6zlF5eVkNQlLeEEz984iovLSqztF0ypyDnUWOh1svTm0ayYOYYFUyos60meR805gNmVvaUtlqClPc6s1z7iqoVrmfXaR7S0x2mLJTodNAUoDrg5pYcxgPlFW5SGYIxiv9va9z0rNvGv+nZ657t59bbz+fu93+TV2863FOZcQ5xzJ5bjdois5fMnV/CL1z/i/Dl/5coFa/hXQzv3v7rpmComsmMejz5sRd3GEYXpQU7oX15JN9FxTwKsMqGELqnZGySa1HEokEjqWdsfTuhpI7C5LqPBmEa+R+fqkaexYv0uIxs9oRHXdJK6RFXAqQjjjlkIkOBIsffGUIxgTKM+GD3ojPFQXKNvgYfGUJy4JvG5jBsGXdr55DY6hT1XZCOntWHB5Ar6FnqIa5JEUs9SVM2IxdlvbObp64YTiiUp9ruJJ3Vmjh3Iyupa7rjoLK5auDZjgPTn48qIJnV8aclPprKt6zqN7fEMG8uCKRXkexyM//17GcvOLgngcCg4HUpOldrpUIjENe5cvjHjvO9cvpGXp1ei6Z2rxEV5rqzPw8x+31DbSl1LJNV5odE3h6XQtPGYQ7KqAK9LtZ4ImIq9EIJfvP6RlSOf/rkeS4q1HfN49GEr6jaOKMwGyyMlBpjFQL38biPaUZeWvUaX2aQ+F77MA8XuqPWN7Qn+/X92sKs1TDCapNDrxKUqKMI4x6I8F316eAwvvZT08rsIx40h1DyXyp59UbbuDbJnX5SEpnWrbbRfoQ+HqjCg2M/gPvkMKPbjUJVjxptu49iDlHKTlHKYlLJcSjlESvlQavl2KeUoKeWZUspJqTQXpJTR1N/PTK3fnravh6WUA6WUZ0sp30xb/mcp5VmpdQ+nLc95DBtHH535zoNRjc+bwkQSuRXVwX0CLJ42iqQuiSZ0fnFFGW3ROF/rm8+Dl59r2UXM7W97cT11rVG+/evVXLlwLVv3BkkkNOpawnzW1E5bNJllY5nxQjWxpOTxSUNZMKWCYr+b1zfUsTcY5bOmdhJJnYXX7494vLishOdvHEU4brQ9m0p4+nlrsmuVONfnce/KTcwcOxAwSKr5U6CziEVFEZQEPJzW00ffQh8989zGzFbKD9+30IeUMqPsyTyWGU15rCjWdszj0YetqNv4UuhOmc7YwSW4VCVjsPJwIanp9PAYCS0dcTBpMkcSsaROIqnjdBg2nNZIklMLPPjdDvbsi9C/l5+6ljCDSvzIVHJMScBDQzBGMGoM4aopP3tjME5CC2Ydo+P3MGZAT1as32WnqdiwYeOg0Dlp1Xn4T59wx0WDciqqmi6Zumhdhnf79CIfwWiSYDSZc5/FATfD+hWwobaVW57/kBdvHm21lq6YOSbna1rDca5auNbKZQe4auFaiv1u7rhoEGf08vHizaPRdZ32uM71f8g8p0ff2mr56UsLvXicCgJx0MOgBV6ntc++BR5+sHQDDaHYIQ9WdqZUt0YSx5Ribcc8Hn3YirqNQ4YZvWhaM3Y0hpixpJoRv3ony089oFfeETkHt0NlXzT55WTxA+CwXH8EII3UGyEM5aUxFCOuSWpTaSf3XjKYt+68kP++91ssm15pvU7XJfGknspvN2PL9mPeXz5lxpJqPtjZTEt7nB2NIVas38XE4X0pCXjYF0lQEvDY3nQbNmwcEOklRiZKC7183hzm7u+czZv/3G0NUJrr5k+u4JE3P8mKIIwldZrb40QTWu59NoUtZbquxfCcm/swhzA7vqapPW5t3xgyIiCL/W7u/s7ZzHrtI7752N+47t/fpz2uM2/Vp1nndMdFg6x9LZhcgUs1Bu6X3DSaRVNHMqxfgaUSF3qdCCFynkdJwM2scWU8+tZWGkNxywbzxDtb2dMWzenv7wqFXmdW4dOcCeWsrK495hTr9CcBxQG3TdKPMGxF3cZBw1Rv13/eggD69PAQjCatC2g4lszyU997yWBueeHDLtNUDhaKgCK/Cwns3hclo67zMKLjdVbBsNQ4FOOQB3pLCsZQq07KiyMk4bhONKlTEnB16j93qoJQLDtnPpbUqdpSz9jBJVRtqefJqm3oqXz4pGb4O4vyXKzZ3ryf8NuwYcNGN5Cr9Gf+5AoKvA5274vxveF9efStrbyUiijc0dBOvteR07ah6ZJ7Vmxi7sTyrBx00+d936WDgUwSDjC/ahtzJpRnFCOZrzHhcxlNobPGlVnbmceeuaSaWePKMs6rriXCgOI8/nbPWLY3tPPuJ3u5cHCJYcNJ8+OfUuAh3+2kpiHEE+9szXkeP16+kQ21rZQWeqlPPdEd1q+AG87rz5UL1hx0dGFLJMG8VZ8ya1wZJQE3freDuKbziyuG0CffY5Phkxg2UbdxUDBVdKMR1CgZ+qI1iiJAQSAUSOgyI14QjJxsRRxmIi2xPNe7OjyaPFLwuxR0CZGEnhXp2BkkIHWJI8XsNd30LLro5fcAueMYz+qdT/VnzdbNjRAYWfDK/mbRBau3k9R1nKrx+FYIQCf1qLnzUqPuWJZs2LBx8sG0NiyfXkk0qfNZU5jn39vJpBH96NPDgy4lv7n66zSFYiQ0ybxVNTx+5dCcto09+6KWxSUYTTB7/BB8LpXWSILH3t5KQyhmWTsWTK4AAQumVFhxj8+9t4PF00bhVAXxpM7ct/eXDwGE44ZSb/q401HXEslSoUsLvWxvMBK3zPM2rTHma2YsqeaPt55HfShm3aw0BOPMGldGn3wPBT4nD/9ps0XSTTsNwMyxA7NuGLo7CBpPalZmfDr+fu83bZJ+ksMm6jYOCmaBkc/lwKUqJDUJwshHF4DUDKU7GE3gdzuoqQ9axN6hqCTQDlsKiw58uqeNUGpQ6GggFNdxCFCV7Oz1ziAxYhhlqoW1wGfk9hblZV64vU7VItdVW+qpawkTN0k6BklXhKB3vtvarrYljFtV0KRB5MH4PZbUOx0cTb/ZOtg0GRs2bJz4UIxsW67/wzqK/W4evKKMSFzjmmf2p7Y8PbmCgBN+csnZPPLmJzw+aSh3vbLRWv/4pKE88uYWSgu91DZHKMxz4nerVvJKaaGXxdNG4nWqvHjzaFra4/zyPzfTEIoxZ0I5z723gxvO68/Cv21j+oUDiWs6t39zEJt3B63X9y308PR1w2kM5c4q75nnspaXFnp58trh/OL1j3lo/Lk8OrGc5vZ4ToIfSWiEYvt99WaGO8D7P/0WD3+vnAcv13A6FELR/aVIffI93Y4u7Jjb7nXZaSo2csMm6ja6jaot9az/vAVN13E7VPJcKi2RBFLfH09oEsovWqMU+Z3Ekzo9vAaxj2s6LodCQtMPaBfpDhSgKZz48js6SOgc3HCHwMh311L2lGK/mwKfi/pgFJ9r//+CZmNo1ZZ67lmxkZZwwgp/NEqZJL0DLhyqQknAUOL7FfrQdJ2mUAIdiRCgSYmqiE4HR9NvtqDzciVbdbdh48RDd+vfpZSWraSlPcGs1z7KUIpvXVLNslsquem56gzVuSjPRZ98D/NW1Vik21TPl90ymldmjEGX0mrmnLo8m9zfu3ITL948mn2RBLd+cyA7G8PMW1VDccDF8zeOIhhNUuBz0hqOs3prA1cM68v8yRUZRURzJpQzv2obs8aVMajEDxh2muKAYZfcF0l0UUYkcam5ox4VRclQx3vlSWuw0twmV4lRx+8gV7vn8zeOyhh+Pda86Ta+GtjDpDa6BVOFNe0XZnpJoddpeajNsh6HIpBImtsTOFWBN5WRqwhBNHl4SDp0L3rxSECXXavpAkMdF2kLVEUwuE8+Z5b4CcWSzLhgAAlNEo4nkdL43UxlWbB6O8Fo0vos07EvkshIb5lxwQCcqkqR34kqIKHpKEJw+9iBnZLq2paw9Z2YSFfzIXtQ2FTdu1u4ZMOGjWMPHYt97n91E3Ut4ZyDj+ZQaYHXaXnB01HXEkFPkXnYrzo//KdPSOg6t3/rTBZNHclrG3ZZg5axpG6QYIfKlj2hrFzzu17ZyMyxA40Mdk2nKRSnvs1Qqx+8ooyGYJzr/7COfK+D2uYwCU1y2dBTefLdfzHrPz5i8bRRrJg5hlnjynjs7a0sr65j9hub0SXctXwj721v4qffPYfbXlxPU3ucldW1zJmQORj71HXD2bMvSiiWzFpnDpiml0EB1mClQxHMmVBuFTOtmDmG528chatDs11Te5wn3tnKrHFlvDy9klnjynjina34PY6cpUk2Tm7YirqNbsFUYXsHPHyxL4KQBimsDxnDP0JAwKUambSansoJlyQ0yZY9QVQBscM4SHosw5m6KAthDJvK1GcC+1XzsYNLeAjjc61Lpb6YivUDr31EUtdxqAoO1Rhd1VK2GQkZ6S0Z+1Ey99MZ+hX6OlXzTXRXdbdhw8bxAV2X7GmLWiquOfh4bSoOsePgY6HXyYIpFYRjGoV5LlbMHENTe9zyj5cWelFEZqzhsH4F/OSSs5ny7LoMuwnAe9ubUBVhRR4W+HL7yksCbn723XNoDMYsFd/0gv/kkrO55pn30SXc98d/Zhzjyb/WoAjIczv4t5f/kaHSt7TH+c3VX6e2OWwdZ37VNu7+ztk8994O60lAzzwXQkjCcY2Ax0F7LMncieU4VYVTC7yU+N3UNISylHDzM1MUhdVb93L7Nwdx+9K0AdUpFVZ2uvFd6NxwXv+sAVWpS0p6eI7ivwobxwNsom6jW6htCVPgdSJcxoVmV0s4wxcuJbTFjEd/eS4VpyqIJCQ984zGtpOFpMP+XHaHohDXDO++UxEZqjkYJDsX6e1X6KMxGEOmfOcOVUHRJUKBYf0Kc+bUHwx5Nttiu8pYN7/vdHRU3W3YsHF8wFTS29N8110NPhbluahpCPH6hjrGDe2bkY9u+sfvuOgsPE7BgskVzEhZTu64aBD3rMjc5+1L1/P8jaO4rvI0PA6FojwXTe1xAh5nTptIwOPE7VAsP7y5n3tWbOKFG0dZsY7FfiMescBrWCx/ccUQpJT08DlYPmMM0YSGpkve+uduzjoln14BFwlNsrMxTGmhlw21rTz29lZmjh1IUZ6L3vkemttjxJM6d6d57edOLKdnys6Tq/wofVi0KM/FdZVnWDc/5jYzXqjOGCjVJFmf/b0rN7F8xpgDfo/dsS3ZOLFgW19sdAv9Cn1EEgYRz/c6M2wnHS8TcU2nNZLE51Lo5ffQ8yTz2CU0iabrgERNDZ763I5uZ5nPuGAAAY/DaCrVdeOXlPjdjsNSWGS2xXaVsZ7+fZvoqLrbsGHj+IBJMNOzyTtLSklv4xx+RpEVqWiuv3flJn5++bkkNZ22qEa+18nyGZWsmDmGfj29OffZ3B6nyO/G5VRSZFjD51KyrCVzJpTjdogMS036fhCCl6ZXckYvH/OuGcbulnYSmk6vgItwXKO5Pc6m2jZCsQTzq7YxbfEHXDi4hJXVtfz45Y2cVuRj3qoa67gbaltZWV1LccCNlJJefjfP/Pf2rBsEv8eBooguG0x1XdLYHkOa55pjGxOyk/cnu4gY7mhb+t5Tf2fr3mC3c9ptHL+wFXUb3UK6CpvU9AxfesfLhEtV0HWNcExje0OI9vixUX38ZZHrvebaBrCGhwaV5HHfpecclOI9dnAJcycOZc5bW9jeaESJndnLd9D7OdAxutpXd1R3GzZsHNswFdhwPGnZPcxMcDMWMdfgo0lIOyPzSU3yg2UbMqwdLoeCu5N2zaYUUd9Q00D/4nwr43xldS2PfP9r9OnhQRWCxlCcxlCMXn5P7kFOAVcvNJT2i8tK+OFFZ3FrhwHSZes+Y9r5/fnhRWdSUx/ithfXM2tcGfOrtqEIuP+yc0hoOo98/2v08DqRYLWhmvtoCMatGMi6lgiJ1FBSZ+2hTodiDYfOGld2wPSWzvbTVcLLgdT89O/bVtxPLNiKuo1uIV2F3ZMa7gFyNoL28rstT3ryBLG8COs/nUMVhj/d5VDY+qtL2fqrS3nrzgsPiVyPHVzCm/92wZfez6GiO6q7DRs2jl2kK7Bb9gQt5VpKyeJpIxncJ8DiaSOzFG1V7CeSqpK7lVOXkmK/QQ5Na4fp7152i6GuL5hSwcVlJVa75s7GdoadXsTMJdUU+93kuVR+Of5c+vX00RpO8Gl9iGf+exsORcHnVljQoaVz/uQKHv7TZouoTqjoZ5F08zzuXbmJCRX9uGfFJpK6ZObYgdS1RDi1h4f7Lh3MlGfXMXH+Gu5ZsQkhBO1xzSo7St+H2ZZqHtsk0GYZVMcBU4ciLBJt3gx13CY9vaWz/XSV8NKVmt/x+7YV9xMLtqJu44DoGNPnd6vke1QaQomcZaCfN4ctTmsOUR7vkNZ/OocuQUhD/T4RcLDedxs2bBw7SFdg51dt4/fXDkMAze0JmkJxwnGN3j3czJ1YjiIErZEEq7fupX+vASSTGq/MqESTkqeuG57R3DlnQjmPvPkJM8cOtLLF61oitIQThKIJy6NuDnn+edMubjivP4+9vZXHrxxKsd9tDXF2HLqcM6Gc371bw88vPxcElge9NZKgR4f2087UfnO5EIKyU/J5ZcYY8tyOnBaeJTeN7rIoqSOBNsugzDhGU7XevS+SkX7z2NtbrVhIt0PhlB7eDGW7s/10pX4fSIXvjuJu4/iETdRtdIl5f/mUJ6u2oekSt0MhqemEYhqFPif5btUaIE2HmfsNB7aKHA9It7w4UkVH5uW04/sr8Dm579Jzjt7J2bBhw0YOpCuwG2pbkVISSehZSSr9enr5+Isg63c2cfnXS7lywRrqWiIsmjqSgMdBSb47gzA/9vZWNtS2ctM39tvgzBjHjgr37UvX89yNo7h7+UaKUx0QcycNpbY5zPVjzuDJv9Zk7Pu593YwoaIf8aROXUuE2W9sptjvZubYgWg6LJo6knmrathQ29qpdcdcvnVPkNlvbGbuxHJcqujUE55rHwU+F6t/8k28zmwCrSgii/h2JNEbaluZ/cZmZo8fwpC+PXIS8Fz76QqmCt8xcca8iTiQ4m7j+MURI+pCiH7A80AfjMjrhVLK3wohegIvA2cAO4ErpZQtwjD1/hb4LhAGpkop16f2dQPwQGrXv5JSPpdaXgEsBrzAn4EfSSllZ8c4Uu/1REXVlnqerNqGnirqSWqG/82lCva2xbpFwrvj6z7WYeSiK/jcDh6bOJRbXviQRAdLjwACHmN9d1XoY7VQ6Fg9Lxs2bHQfHcljgdfFlGXrciapzH5jMwsmV/DbVZ9a630uNeUtdzH7jc1ZZDYc31/wM2dCOW3RRE6iKICfffccvC6Va9OaTZfdMjpnRGGBz4mmS1ZW1/L7a4cRiWsZKv3cieU8+tZWVlbX8vTkiiyP+nPv7bBKlqz3eNMoFk0dic+l0hoxBk0bQjEaQ/GsJwbGEwYoLfB229+di0TPnVhO73zPYSssOpAKfyi+dxvHB0RXU8ZfasdCnAKcIqVcL4QIANXA/wGmAs1SykeEEPcBhVLKe4UQ3wV+iEHURwO/lVKOTpHuD4ERGJyvGqhIkft1wI+AtRhEfZ6U8k0hxKO5jtHV+Y4YMUJ++OGHh/+DOI5xzcK1rNvZhCoECU0e94T7UKEI6N8rj5KAh2XTK62nDAnT1iPB6VC4fexA7vj2Wd3ap1koZBZCmcOa6T7wr4Iwm62owWjSyHJXFAIeB3MP4gaks/3a5P/IQQhRLaUc8VWfx9GEfc3uGh3bL//y4wv59q//lrXdX358Ad/+9WpKC73W0OVdF59FaaEPj1MAgoZgzLKOlBZ6eXpyBaFowrLMzK/axsyxA3MS+hdvHs32hvaMZlPjuBdasY/p2780vZK/fLybr5/ek3yP02rq7LiNlLCjoY3+xfkkdR1VUXCqsKmuzcp6N/HuXRdmNH7OnVhOkd9Iilm69nO+X1HKqT08qIrA61Ip8B78EKaZ+hJN6KiCQ97PoaKztlO7NOnYxMFcs4+Yoi6l3A3sTv05KIT4BOgLjAfGpjZ7DqgC7k0tf14adw5rhRAFKbI/FnhHStkMIIR4B7hECFEF5Esp16SWP49xI/BmF8ewcRCobTG85vETZCC0I7qr9usS2iIJZl1WBmCR8SertpHUddwOhR4+JyvW76K8tKBbBPRAfn9/CwAAIABJREFUhULpRD69GfQhOKIEd85bW2gJJ4xWVFVBSmgJJ5jz1pZDPu5X9V5s2DiZka7ARhIamq53kqQiGNavwCoxuu/Swdz1ykaK/W5+csnZ3LNiE8V+N7PHD+G0Ih+KgMZgPCMvHWD9zqYshfvpyRUsXbuTb53TJ0ttD3aiwLdFElzytVOJJDSkzB11uGdflN75bnr6vVbeemmhlwWTK1hZXZtB0ksLvXzWFM56kvDS9EruXfFPGkIxbjjvDEoLfV+K0CqKoCTw1ZUVHYrv3cbxgaOS+iKEOAMYBrwP9E6ReJPMmz+p+wK1aS+rSy3ranldjuV0cQwb3UDVlnquWbiWvfuiJE+MWdCcOJjbj47brtneTGmhl7JTejCwJEAvvwenKliwenu39lfbEsbr3P9Isi2SYM++KOt2NnPpb1Yz/YUP+bw5zPaGdmr2BtF0eVD7P1Rsb2xHEaAIgUCgCIEisGIiDwXpNyVCGL8fjfdiw8bJCl2XNARj7N5nkFO3Kvjxyxt5fNJQK2nk4rISXrhxFMFoksevHMqM/3UGPbxO7kqV/cwcO9Ai4xtqW5m2+ANu+MM6ADxOhbkTM5NNrq08g9+t+pRZ48p4eXols8aV8btVnzL8jCLLN56O1nD2stJCL83tcRpDcaY8u46a+lDObZra4wgBDcEYj08ayoIpFRT73cxYUs39l5VlnNfT1w1n3qqajH3UtURI6pK5k8qZPX4IpxR4TghCa/re+xb6KA64T4j3ZOMoDJMKIfzASuDfpJRtZr50rk1zLJOHsPxgzm06MB3gtNNOO5iXnrAwbR1JXecECWz50sh3q/TwOi21G758c2e/Qh/1wSg+l4O2SIIvUj9QHQI+3RvEfIghMWIua5vDKTXs+GsGtVtObdg4ekgmdb7YF6E+GKOpPc7K6lp+9O2zKA64WFldx9JbKglGEyQ1yZQ0O8j8yRU4lP1Dl52lqtS3xXjkzS385JKzWXZLJfGkzu59RqnRf22uz0hmAZh+wUAe/tMnVn67ebyeec6MVtPSQi+PTxqKIgQzU8vSc9/Tfeirt+6lb4E3YzDW9KXruuQ3V32dQp8Lt1PBpSo0hGIZ51Ra6KWuOYzHqdKnh4cC78lVymfj+MIRVdSFEE4Mkv6ilPKPqcV7U5YW08du/l9dB/RLe3kp8MUBlpfmWN7VMTIgpVwopRwhpRxRXFx8aG/yBEL68KhTtSP2TUSSOklNzyCWX7a5c8YFA0hoknA8SWPqh4hAIIQgPfbWvK/Vgb1tsSPeDNq/yIcuDUVOSomuS3RpLD9U2C2nxweEEP2EEH8VQnwihPhYCPGj1PKeQoh3hBA1qd8LU8uFEGKeEOJfQohNQojhafu6IbV9TSoMwFxeIYT4Z+o181IhAp0ew8bBQdclW+uDXPvv7zNx/hpmv7GZG87rz+sb6vj55ecyY+xAdF3S0h63YhHBIOAzl1SjSyw1OpcKbqaqbKht5Zpn3ueL1ggX/fpvTH52HfXBWM7tiwNuGkIxHnt7K7PHD2HVXRfym6u+DoDbqTB7/BBenl7J7PFD8DgV+vRw54w6fPeuC1l2SyWrt+7lqlGnW2TePP97V27ijosGsbMpTCypc/crGxEYvR4LpmRmsj8+aSjPr9nJqQXeg/Zwm08rdrWEaQjGjpmc8mP1vGx8eRwxNpa6AD8LfCKl/HXaqtcB88J9A/Ba2vLrUxf/SmBfyrbyNnCxEKIwdfG+GHg7tS4ohKhMHev6DvvKdYyTElVb6rnkib9x9gNvcvYDb3Lpb1ZTtSX73mXB6u0kkjqaLoklTh45vatLtAAUBLv3RWlqj1ufYW1TiLZIgnA8iZQG4T6Y5s70QqFoUsepCE4t8KDJDkO7prIuIaHrR7wZ9L5Lz6HA50QooEmJUL585GT6TcmhfFY2jhqSwF1SynOASuB2IUQZcB+wSko5CFiV+jvApcCg1K/pwNNgkG7gQYxQgFHAg2nE++nUtubrLkkt7+wYNg4CTe1xZrywn8AW+924VIXLv17K1QvXctHjf2Pys+9zSoE3p1oeS+o8c71BaudXbcuyt8ydWM78qm3Wa8JxzVqfq+hn0bSRqIrgxZtH87trh3Fu33ziCZ2eeS4iCZ2piz5g2uIPuGrhWqYt/oDbl27IuFmA/VGHALPf+JjJY/oTjCZznv9pRT7mraqxSLvLoRre7ZIAS28ezWu3n8+iqSPxexw8cFkZJf6Ds4d0p1ToqyDMyaTOJ3vaDmvZkU38jx0cSevL+cAU4J9CiH+klv0MeARYLoS4CfgcmJRa92eMxJd/YcQzTgOQUjYLIWYDH6S2e8gcLAVuZX8845upX3RxjJMOVVvquXvFRlrDCczrUU19iHtWbMxK8vhwZxM6HP95igeJA73dpK6jSdB0DTX1Ie4JxvE6FXr53eyLJCg9hCQTs1DomoVrLRuMS42R1DTL2yWEQdIVAYOK/Ud8+HLs4BIemziUBau3U9cSPqT3lWufD8Fh3aeNww87AOD4h5mlPaxfAb+4ogxVUWgIxrhz+T8o9u/PQ5fS8Kin21RKC73sbGxnUG8/L948Gl1KPE6FJTeNpjEUI5rQ8LlUigMuFkypoCjPRe98N7+9+uv86KV/sKG2lefe28HSW0aj6ZJgNEk0rjFt0QeWPeWp64azZM1nvLe9ieduHJWTbOtSsmDycH67qoYJFf0oynPRy+/mP/+xi//aXM+9l55DzzxXzsHY3a0Ra5D0jF55FKYsdw6Hwqk9vARjSaYt3n8+B5uKcqBSoa8ieUXXJV/si2TcoH3ZsiM7QebYwpFMffkfOhcrL8qxvQRu72RffwD+kGP5h8CQHMubch3jZMSC1dsJxZKoQqAogqSmo0lJQyjOHS9t4OZv9GfN9maqP2vmJBLRsyCAAp+DeFKSTEVRmvGLujTWO1QFJeVFEbokoUsKfC7e/LcLvtSxZ1wwgJ+//jHheJJefhd1LRG0FDlXFcMKczSLlI5EI6ndcnp8oasAACHEEQ8ASDuGjW5A142OC01KXppeSYHPgUtVuf4P63h80v420HSv99OTKwD4r831GR7v+y87h4nz11i+dSGgOOBG0w3i/sNvDeJ379Yw7fz+JHVJL7+bpTePRlEENXtDfN4U5r4//pNFU0dapBgM8njbi+tZekslEypKcaoiZ7b5zsYwZ/X289PvnsPOxjAP/+kTGkIx5kwo5+KyEj5vCjNvVQ1zJ5Zn5Kub5w+mIi+paQhZ5LIlkvjSZDa9VGhYvwJmjh1IgddJPKlZ8YxHux20qT1OfTB2WMuO7JbTYwt2M+kJjtqWMJouUYVB0hNpj6/aY0l+++6/CLjVEzaCsTtQBIwfegrVn+8jntRoao8D4FAEvQIu6oMxpL7fLw7GnzVdWr717uSEd7ZNR8X5rN4BgtEEDSHjPM7s5eO+S8+xia6NowI7AOD4QjKps7U+aJFQU7nWdINUtkYS3HHRIIukg0G8bl1SzeJpo7jpGwOsxtGGUMy6/pm+9ccmDcXrVNkXSeBQXUQTOvd852wrmcU85hNXDmXeqhruu3QwdS0RVCV3G2giqeNyKGi6ZNm6z6wbhbkTy/G6VH75+uaMmwWTgN+7chPP3ziKu5ZvZENtK4++ZXjeBxTnAfDwnzZbEZNPXTecp/+6jfe2N1nk8nA0d5qlQrlufBZMqaDQ57Sy6E1l/0i3g5o/sw5n2ZHdcnpswSbqJzgCbgdf6JJEWrGV+dNSAqqAtmjyKzm3YwGmvaT6831MHN6XNdubSWptxDWJy6FwRpGfHh4nO5vCSLmfrEtpqN2lhb5u5YQfaBtbcbZxLKCrAICU0t3dAICxHZZX0Y0AgBzHyICUciGwEIzCo0N6kycQOrM9/P7dGh4Ydy4rZo4hmtA4vciXk3ipClYco0mKX9uwiyU3jaJPDw8uVcHtUAgnNHp4ncSSOqFYklAsmVFgVNcS4c7lG5k1rswaQtV0mZM8ft4cZtriD6zjNQTjbKht5Z4Vm3jk+1/Lulm4d+UmZo0rY8YL1exLDbICVmTkipljePhPn3DvpYO5/7IyVEXQEIxSUx/KIJeHo7nTbCDdsy+adeMz44VqZo0rY/Ybm62bC/PG4Ui2g7ocKiura7PScUx70qHu0245PXZgE/UTGFVb6o1Yqg4/ziSGWmz8RXIyz4hIwONQcaqCNdubWTa9Mmsbs62zJZxACuPD0iUUuJ3MuGBAt8qL7nhpA+3xJB6HSnHATcDjzNrGbu608VWiGwEAj5AdAPADIcRLGIOj+1JE+23g/6YNkF4M/DQ1bxRMhQW8jxEA8LsDHOOkhWln6aq8xrQ9pPvPdSlRhODatCKg+ZMrcnrSnarC0lsqaY8lKfA5CUYT3P6tM2kIxtjW0M7K6lqmnd+fR98y1Pa5E8sp8DmJJ/WcxL8k4KY9luS5G0cRjWs8fd3wjEbTuRPLefStrdb26SS8riWCx6lmWFjM7Qq8TiNxJpzIOKaZqQ7GE87r/v39rKjHpC7Z1RImz63yzPUjsnzXB0NmzVKhPLea8/2bkZbm+5r9xmYWTD50wtwdFOW5uPN/n80T7xjpOEV5LkoCbk7t4T1kP7l5Q/JlPisbhw82UT/O0ZHgjRnQkzXbm6ltCdMWSeBzqfTr6WNXS5hkGiHXdHmyzYx2iuKAOyPXOxdpnjtxKHPe2mIV/6TbUR547aNOc8JNJT0c13Aoxg+NL1qjnFoAfrcjYxu7udPGVww7AOAYga5Ldja181lTGJ9LJRw3VPEzivIyyJdA0rfQazWI1rVEWDR1ZJbaPXNJNS/ePJrNu4MZ9ph5f6nhve1NLJ42krZIglhS58bFH2Yo7Iv+voNfXzmUT+tDLPr7Du6/rAxNl6yYOYam9rhl87i4rMQSIXa3Rnh+zU4eSJUfJXWJqgh+uHRDRmuoSW7BIN2n9PBQ2xLhvksHZ/jWw3GNpydX4HYIS+lNt8XMHDswS+G+65WNzB4/hElz11g3CaWFXv5423kp+82hNXcqisDrdORUnFsjCev4g0r8R6VMybx5ePh75YetkdRuOT22YBP14xgdCd7OphDrdjZT7Dem5L9ojRDsxNZyMpB0wYHfpyKw1O0ubSxXnJs1NGo2uDYEYzSGYvQOeMhP/dAxc8JNtd3tUEhqEkUR6BixV6oi8Lsd3PHSBsJxDbfDSJHJ92aq7TZsHA3YAQDHDlojcfa2RTMKfUw1u2eeMcyXTOrsbovRHktaJB3A58qt9raGE5bq3hpJ8Pt3jVSV5dV11DYb23ck+KYyXB+MsbK6lp99twxdSj5LDXSaQ56rt+5l3NC+XL1wbYai/as3NnPNqNNxqoZNMFfxkGmVeeq64bSG49ydZsWZO7Gc4oAbj1OhtjnCG1v28sJNo2gNJwh4nDz61idsqG3ttJzJ51KtP9+zYhOzxw9hSN8e9P2SHQ65FOeOw6yfNYWPWpmS2Uh6rO/TxqHBJurHMRas3k4wGmdfJJlhX9nTFmNPW6zzF54EMEm6qWLnWq8ohu0lPdf7QDYWE+mEvk++m12tUXa1RgCJQ1Ws/Zlqey+/22gf1QEhiSZ12iIJJNAeTxrnqUmroTTgcdjNnTZsnKSIxLUM8l3sdxNN6ASjSZK6xKEIogkNr1PF34GYm8S3o9q7py3KjBeqgf2JJYNK/CyYUtGlnaUoz0VC07nhvP5Mfvb9LGJ678pNLLulkmtSVhvzdXe9spFFU0fiUAQOVcGpwoIpFVlDrwKYNa6Mnj4nVz/zfoaNJxzXCMc1vv3r1dYx/9+fP+HBy8/F61K583+fzebdwU7fs6lwm+fkc6kZA5EHshd1tr6j4qzpkl+lDbMumFzBKQUGSbdVaBtfFjZRP47x0a5WgjF7CjsXTKIuBHgcCgnNyEI31xmpLdAe19jdGmXmhQO6tLHU7G3jmoVrLTtMS3ssg9ALIdizL8qethjDTyu0POb9VvuoD0Ytpb0xFCOWlOS5HMYPQF0SiWskdUNtRze2caQUKBs2bJx80KTMiAHsmDDyxJVDcToUfrB0A7PGlWWQVLOoKD268Mlrh/PkX2s63d+Sm0azo7E9J9ntmefii9YI9/3xnznV9hkvVJPQcpP8cFxj/JN/t84hz63y8vRKq0Duwdc+tsjt4mkjc8dIXjecZbeMxuNUCXicPDCuDIACr4sCr4tXbzsfXdezbgLS/fDmewnHNWsg8kBZ4Qdan64467rk4e+V8+Dltk3ExuGH3RN/HCNkk/TOIaDI50ARgoSmZzxxkOzPRnepgrim8/zaz6jaUp+z7r4xFCMY06gPRi2L0Za9IXY0tLO9IUQwajyGPbPET0nAzbLplZb6nt7KGfA46NPDw6kFXuZdPYxQ3FDEigNupARdShCSWFK3mztt2DhJoesSp6JY7Zy5/Nd3Lt9IS3uCupYI86u28eS1w63tG0IxvC6VxyYN5S8/voBZ48r486Zd/OBbgygt9Gbtr9jvZl8kTs88Z1YT6VPXDWd+1TacqtLp8GRpoTerTdR8fcDjYFi/AupaIty+dD179kX5ojWKx6ky+439CvScCeXs2RflZ989h3hS5/FJQ1kwpYJiv5tbX1xPNKHzvafeY+qiddQ2R/jlf37M1r1B4/wDbnr38HJOn3xeve18/n7vN/njbefRO99jWW1M4n56kc8aiOwsK9wcTj3Q+nSYpL1voY/iwMG1ndqwcSDYivpxiqot9SeFz/xQoUuIJHTcDoVYcn+Tk4JB1E21XVUUhC4JxQx7i1k+1BCMEowmiSWNgqgCrwOfy0EwmqApZDxOlZAxHGrGNaajq1bOfqt97GgMEYwm0aRE6hIpDdvLQ1eca/vTbdg4yWCquE+8s5XHJw3lrlc2dum/Ni0s+V4Hi6aOxIyrD8WS7IskyHMZhHjuxHKklFbuePr+Zo4dyO1LN1Dsd/OTS87mhRtHoUvwOBUaQjHe297ERWW9c6rt4bjG3InlOB2Cp64bzm1pCS9PXzecplCcn1xyNo++ZQx8lvb0saOhnVc+rGX2+CGc0SsPVYFfvbGZAq+L6887gzuX/yPLXpPuNTeV/I6NoB0tKr3y3PzxtvOIJnRUAV6XmmFF6SorXNclkUSyyyzx7qTy2LBxOGAT9eMId760ntc37UE7mfMUDwLhhE7HutX0v+kSkpqOqggSmmT95y088NpHII0LsC4lblUhlpSEYhptkQSNoRhCgFMRJHSJpkuSumRnUxi3Q2H80FOzzqOzjPQxA3qybmczigCHAro0Wkhv/kZ/m6TbsHGCIhfBA0PBjSSS7NkXpSEYZ2V1HYumjsTjzJ1pfVqRl4fGn2vFH15cVsIPvzUoIw5xweQKXrx5NM3tcfK9TiSwe180Y3/mjUBdS4RrnnnfOsZf7x5LScDNS9MrcakiK2px/uQKivwuGoIxXKqgKM/FI9//GqcUePm8KczPX/uYhlCMpydX8OAVZfxg6YYsAt4QirF8RiWzxp2LEFjDqLCflM8ePyTLa26es0mqO7OolAQ8WZ99QzBGPKkhhMgdWelQ2Lo3yJ4On5O53uVQD2iLsWHjcMIm6scJ7nxpPa/+Y/dXfRonHBK6tNpaNV3SkvoBKhH0K/QR8DjZ3hAiruk0hmLEU8ReEQKnCsmU8V0AhT4nK9bvory0AOCAuehrtjdTEnDRFkkS13RcqkK+18Ga7c3ccVQ/BRs2bBwN5CJ4C6ZU4HYoTF30gbXsD1NH4HM5iCd1BJKF11fQFknSy+/C41QQCJIaFnEGmFDRL+PvdS0RZiyp5oWbRlEfjPF01b+46RsD+GB7E/MnVzBzSbXlI89FSHc2thPXdEuR7+V38Zurvk5JmrWjrjmCLiVbdoc4o5ePhCa54Q/rMvZ165JqZo8f0qm/PZLQmfPmJ/z0u+fkVLBPL/Jx1/KNGedmDo+6HGq36+5zffbzJ1cAWO2oz1w/AociuOX5Dyn2u7NKhJ65fgSFXid72qK0x5IZLaS5jmnDxuGATdSPE/zHRpukHyq6E9MIhkqe1CQJHVRhKC8Bj5PigJtdLRFiSR2XKixirwAuhwISHKqgOOAhHE/yyJufEE7oB8xFr20JU5Tnppd/v+ojpbTTXmzYOEHR1B63imnMmMTf/uVTfvCtQSyaOhI1RYAj8SQPvf0xEyr6Wd7vu1/ZaNlT7lmxiccnDc0gtrksMsV+N0gjTvC+S8/h5XWfMWnk6cSSmnUOpxZ4ePq6Cm59sTpL8b7v0sEZ0YZel8q2hnbmrarhN1d/ncI8Jx6HYrRcK4LTOmlANa0r6csGFudxcVkJnzeFmVDRj52N4Zw3DIoQGV7zORPKee69HVYBz+59kS4tLObTCyEET7yzNStffvmMMTx4ubSebpj7q2uJ8Njb+7+r0kIvvQMeahpCOWMZN9S2ZiTK2LBxuGAT9eMAVVvqkbbb5ZDRnY/OpQpUxZitFpphi4lrhlEm4HHSK6DRHtNwORSC0SSFPieNoRhSN5pdk1KwvSFEL7+Lz5oiOFQFTZe4VCMb3amKrIjHfoVGIoyZHAP789dt2LBx4kHXdW76xgDuSssK/81VX8fnUi1F/eKyEu6/rIz7LysjoUmC0Tg/XGb4tudOLMehKLxw4yiEELw0vZI5b25hQ21rVkThsH4F/OSSs5mSUrjN5BWfS8GpCs7q7UfXjetjr4CT2eOH4HOptEYSli0lvcDH51Lp08NDQzDGg1eUZbSAzplQzvqdzYwf3rdTL3s6Sgu91DZH+OG3BqFLScDjZG9bNCu5Zc6Ecpa9v5OXp1eiS6P3QhHwi8vPxZMi/53V3XtdapaCPmdCOQ3BuFW6VNcSQUqZkauevr8Nta3MeKGa0kIvr952Pi2RRJZ6n95CaibK2LBxOGGnvhyjMMt0vjHnXe54acNXfTonHASGgm7+2WhPN+BQhKEQCYGURmKLU1W5+Rv9KfYbg0t7gzGSOmjSUJJMNb6uJUJSNx6zqmJ/NnpS07OU8vREGPM4dtqLDRsnLjSJRdLBIHr/9vI/aAzFqWuJMKxfATec15/r/v19LpxbxdRF63CqKsV+N1dWlFIccJPUdab8YR1jH6vi7lc28rPvDmZYvwJWVtfy9OQKS4G/46JBGVnsZvKKlPBFa4Qpz67jol//jWufeZ/drTFOLfBw1ysbmfFCtVVkNL9qG7CfbG/ZE2Th6m3ke5z87pphVjLLvSs3cWn5qXidCi/ePJoVM8ewYEoFF5eVMH9yBaU9vRlpMnMmlDNvVQ23vriePLeTu5Zv5J4Vm3A7FJbdUsmKmWOYNa6M597bwQVn9+aX//kxdS1hfrB0A5MWrGVHU5j7Vm5i694ghV4nz1w/ImP/z1w/gqQuc5LqmWMHWt+HaZ9Jh1lm1HF/RXmuTgdQzdeY8wY2bBxOCGlLtQCMGDFCfvjhh1/1aQCZZTpep8one9rQ9AO/zsZ+CAzlRcvxz9uk5GbyS6HXSSimIYSZr2686MxiP6GY0Vg6ZkBPXlj7GS3hBOasUDy1c6PQQyAlxJK6EfvoUFBS5F/XJUKBYf0KWTa9MuNcqrbU50yEsWHjYCCEqJZSjviqz+No4li6ZncHui6pbQlz4dyqrHV/+fEF3PPKJh6dWM60xR9kqcPP3zgKhyrYVt+e0R5qrl92SyWaLnE7BVt2h+jX0wsIvv3rv2Ud6693X8iUZ9dl7eOR73+N9rhGScBNzzwXD/9ps+XdfvLaYfTwuogmksSSktuXrs9Q6RUBRX4XLeFEhiL+5LXD+fOmXUwecwZSwr5IAo9TJRRLUh+MMb9qG/dfdg6xpM5jb2+lOODil1cMIa7pKEKgS8nrG3Zx1in5FOW56OF18pMVm2gIxSwVe/mMMUgpcaoKSU0nKSUep4rUJaP/37tZ73/FzDFMnL+mywHQzhJdGoIx7n91ExMq+lnWpZXVtfziiiH0yffYg6Q2uo2DuWbb1pdjDFVb6rMq5T0Olfa47X07EBQBvfPdNIbiII3SEBNqirQ7FIGmS8sO83+GnkL15/twOxX2hRPENB2HonD72IHc8e2zrNdfs3AtwWjSGiQFQEvFdEmJpoNLVUim2pSkBB2JECCRJDVyKuWdJcLYsGHj+EcioVEfipHUpfHkTRWdJIko3P2dswnHcyu2ze1xivwufB1aSM31SV2nvi2GqgimLf6AYf0KeOKqr+c8lkPJnYme53bQHjdaNoWA6RcM5CeXDCbP7aAhGGPys+9b5LijSj9rXBmNoXjGTUT6uoQm8bkMgp6eHDN3YjkJTefelZuYO7EcRQgmLViTsf47X+vDY29vtW4aTE+46cn/ojXCw3/6xPLupw/p5kp16eF18vL0SvoWenEqgqb2eFa0YnqZUToKvU7uuOgsaxDXHEgt8dvZ6TaOHGzryzGEqi313LNiI22pmmjjoh22B1S6iZ55LpyqisBIc5GpUiOBQdKdqsDtUHCoAo9TYXBvP09cPZyHrjiXM4r8FPndjDqjiAWTKygvLbCsR9csXEtNfZCkrpPmkLGUdVURDO6Tz4BiP07V8H8W+pwkNJ1owigvOrWHxybkNmycREgkNLbUh7hq4VounFvF1QvXAmSVCs2dWI7PqaIqgh4+J4umjmRYvwJrP6WFXpra4+xsDFsJLekoLfSS1CR3vbKRpva4td7tFBlFSCbJBZlzH4U+F7Pf2MzE+Wu49pn3SWg697yyifaYZuWjd5bpXuB1dnoTMajEjy6NJ5UdrTj3rNiEphuWwT75nixb0D0rNrGrJcpPLz0nw2bzs++eQ888FytmjjFU9jSSbr52xgvVPHBZWdb7/8mKTdz1ykZq9oYY/f/e5XtP/Z2te4PourTiG3e1hGkIxtA7RCG3RBIWSTePM3NJNS1p8ZE2bBxuHDFFXQjxB2AcUC+lHJJa1hN4GTgD2AlcKaVsEYZB+LfAd4EwMFVKuT71mhuAB1K7/ZWU8rnU8gpgMeAF/gz8SEopOzv2F6HVAAAgAElEQVTGkXqfhxNz3tpCSzhhkMHU9UGToNvupC7hUo0ao2A0yWMTh3Lri9WW58X0myd1CVLSv1cekYRGQpPcd+k5QLaqnW49MlNbgtEkijDUeE3XkXL/kKrpZY8kNAIeB7GERlN7HEeKyRvlSxpVW+ptsm7DxkmC+lCMWzuQui9aozz6Vmbqy6vrd3H9eWdwd9qA6dyJ5Tz6ljHQ+eS1w/nF6x8zqMTPLRcOYO7E8izlOOBxsHjaSByKwiszK6lrjrBld4hl6z7LONZz7+3ggXFlWfuYP7kCVYHzBhSxvLqOupYIi/6+g99fO4xkikgDWQOrsD8u0fxzx3U19SFmv7GZF24alZPIO1WjhVWTdJoYUx+MMfuNzcyZUM5rG3bhdamWRcgsVyr2uzNeX9cSQVUEf7ztPMIxjR2N7daQrPn5mtvd8vyHvP6D89nbFusyG72rkiQbNo4UjqSivhi4pMOy+4BVUspBwKrU3wEuBQalfk0HngaL2D8IjAZGAQ8KIQpTr3k6ta35uksOcIxjHtsb21EEOFPpIyYRtHm6AUUYFpaMZWBFmoFBuov8bk7v6TUiwQS4HQrFfieKorAvkqAk4Omy+XPB6u04VYHP5UAI4/dCnxNNShKakfKS/p0UB9zWfudOHMqpBV7Dsw44VYW+Bcbj1gWrtx/2z8SGDRvHHvRUEVpHUtfUHqchZHizWyMJCrxOpl84MEulvWfFJuZdMyxlB4ENta1c+rVTmPvWFgBeuHEUf/nxBTw2aSgFXicT56/h279ezeRn36chGOeZ/97OvFU13HBef2a/sZmrFq5l9hub+cG3BvHu5j2c0cvHS9Mr+evdFzJ7/BBm/cdHXPPM+0wZczpXVpRaQ61XLVzLlj1BS5WeX7WNORPKs1Tq+VXbmF+1LetpwZPXDmd+1TbqWiJW/GI6zCHVp68bjkMRna5vjSSsYdBcn9etL67njosGZb3W5VApCXg4raePIX178Ptrh/Hy9EpeXb+LmWMH8vL0ShZMqeC8AUVE4pqVjT6sX4FF4Jva49Y+zUSYXMexYeNI4Ygp6lLK1UKIMzosHg+MTf35OaAKuDe1/HlpTLauFUIUCCFOSW37jpSyGUAI8Q5wiRCiCsiXUq5JLX8e+D/Am10c47iBQTwVKx4QwK0KYrkmI08gOFWBrstOB0DNeK6OK/RUROKZvYyILTP2cECx39osHE9yZokna5gzF2pbwhR4nRnLevndNIaMBr5E6gRdqqAwz0VpoS9jvw+89hFnFvszkmTsfHQbNk5sdMzsNomnmeYyc+xA+uR7WD6jkl0tEe5cbijoK2aOyanS7m2LAqQG2r2cXuTjvzbXZ3iugYzX17VEuO3F9Tzy/a8x+dl1GTngfQu9uB0K5w8qIZYwbiSmLupQTvTiehZNHcn2xnar6Mck5/eu3MSG2laee28Hz984iva4Rp5L5ZE3P2FDbSulhV7yPQ4e+f7X8DhVeua5rDIggHmrajKKlkwlPxLXeGHNZ1xbeRpPXDnU+lzMJwtel8ovX99svT+nKjotRjI/7/RyovQmUociEEIwaURpxnGeum44v/zPj7N88B2z0c10l46qu532YuNI4mgPk/aWUu4GkFLuFkKYkmZfoDZtu7rUsq6W1+VY3tUxjmlUbalHFYJwUkdoGg5lf1GPWxX06eHls+YTl+gJAXkulYRmWEjS7T7mICiATA2E6tIg57oEoUCB22lZWWZcMICfv/4x4XgSr1O1rC7djT3sLN9cVRQGlRyYgNv56DZsnDzQdUlje8yyV8xbVUNDKMaiqSN4+rrh/O5dQ9k2ie+iqSMzhi5NX3lHy0hTe5zZb2xm6S2jWTzNaBftbLt01LVEOLXAy7B+BRk54I9NGkqe28GtS6p5fNJQigPunIRXVQRFeS5r3YbaVovwDyrxI4AfL9/IhtpW6wbkpm8MoCTfTTypUxxw43c7LOJroiEUI+BxsOyWSnQp2b0vSoHPwbjf/Q8LplTwg6UbKPa7eeT7X6NPDw+qIlAVwY+W/cMi+8YwbO6BXK9T5Y+3nUciqeNyqBR6nTnLiVRFWDYj8z3f9qIx9Ppfm+u7zEZXFMHZvQO8etv5WYkwNmwcKRwrw6S5/pXLQ1h+cAcVYroQ4kMhxIcNDQ0H+/LDhnl/+ZQZS6qJpe7cJZDQM60vSf3E88CZg55ORaAAkYROnx4eyk7Jx5HKJvc4FByKkqGkmxdwc5FLVbi+8nTLyjJ2cAkPXXEuJQFPt6wuHdFZvnn/Ih+RROb3kE7Azez7T/e2UdcSoTEUtfPRbdg4gWHW0n//qfcY+1gVs177iLu/czbFfjfTFn9Ir4CLBy8/1yLpQNbQZVd2EsP/rHPPKxvxOpUsa8lT1w1nZXVtxjmVFnr5rClsWUHM7dwOYXnm9VScoZl3bg6vmgk0p/TwcHHZ/uvlhtpWZr+xmZr6EDubwlZTqHkjcNcrG/l0b4ibnvuQnU1hfvmfH/Oji87KOteH/7SZ//XoX7nu398n4HbgdRpWEnNIdUNtK5OfXce3f72abz72N5KazGglnTuxnDy3mjPnvHe+h5KAh76FPooD7k7LiXr5XTlvUNKfonaVjW4mwpjHsUm6jSONo62o7xVCnJJSuk8BzNvtOqBf2nalwBep5WM7LK9KLS/NsX1Xx8iClHIhsBCMTN5DfVNfBvP+8ilPrKrpsnk0rkl2tcaO3kkdAbhUBVURRBNaxg2I+dTA41Txugw/YV1LGJ9LJc+t0hiKowqBmsrUBaPdL6EbynrfAg8OVWHF+l2UlxZkkPVDHdwcO7iEhyAr3xzoVKlPH0A9pYeXxlCM5vYECU0yqCRg56PbsHECoqk93mlT5YwXqoklJJrM9Kp3HMg07SRLb6mkvi1KU3vcsl2YxHlDbSu/eH0zT00ezkvTK60Ixd+v+hc//NYgNu8OZqjGj729lceuHMrL0ytpjST4/bs13H9ZmWXDUYTgmmfWZrzmufd2MO38/vxw6QYaQjGenlwBkGEHee69Hdz2zTN5fNLQjHZV85gm4f2vzfX88opzefHm0TQEY/TwOpn79hZLYa9riTBjSTV/vPU8nrl+BHv2RXOq5E5V4bFJQxFAOK7RO99DvsdFvseVpWoDls3F5VA7HfxURW5FvjUtuaW00EvfAi+97Wx0G8cAjjZRfx24AXgk9ftract/IIR4CWNwdF+KaL8N/N+0AdKLgZ9KKZuFEEEhRCXwPnA98LsDHOOYQ9WWep6s2tYlST9RkNB0VMWIIEum+VrMRJaAQzCoJGB5vU3iqyqGb11RBGpKBYomdRQBfQu85KdUkHA8yYLV2w8bGe6M6Oci8GMHl3DNwrXWACpAccBDnttBSaB7vngbNo4F2GldB4fOyGCB12nEJuqS2uZwBjE0hy7TU1emnd+f36+q4XvD+1o55aaCHE/qDOtXQEMoxpbdQaYt/sBSqFsjcUKxJLPHD8HnUmmNJKziIKeqUBxwUxxwc2uqjXPR1JE4VZEVg3jvyk0snjaKe17ZaNlMbl1SzbJbKrn/sjJUxUi2+uml57CnLcqr63exeNooWsPxrBsL80ZEk4a/vne+GyEEN31jABMq+lm+9bqWCNGkztm9A/TOd7NgcgUz0vzrcyaUM/uNj/nFFUOQUmbZTNJzzs0nG+k2l6U3j85JyPe0RXOm3sxb9am1zdyJ5VlPT23Y+KpwJOMZl2Go4b2EEHUY6S2PAMuFEDcBnwOTUpv/GeNi/y+MC/40gBQhnw18kNruIXOwFLiV/Rf8N1O/6OIYxxwWrN5utWAeb0hLkOzWNhKI5rjwmWJFOK5nWENMVfuRNz+hpiGEM6WCOFSFnU3tlKaRdACvUz0qw5qdEfhcA6hH65xs/P/27jxKqupa/Ph335p7gG4aGpAZH6CIIIOIw0o0TjEaeUaI8/xTI4lGM/w0KzGTcS3U53u+GA1qgkMcECVGotHoU3nmZ1CgERVBRVFDA9LN0E0P1TXd8/vj3ltd1V3VNjTQ1d37s1atqrp169a9Z8HpXaf22UftQw8BvwMeydjmVdKaLyI3uc9vJLta11E4lbiOyqjWNQPnv36ViCx1A2+vWtebOP3+13H67nyfUdAkz+isV8kkGk/y21c2pCdjVu+KUtsYIxL0Mf9bhxPwWQzuF2ZrfZSzpw/HNobH/s9RfFHfQl00kS7ReMvsSYQDVlZJwXmPreaRy2cy/4X1XHLMmHTwfcrESq49cTznZ4yY3zFnMtcvWkNtY4yHL89dJnFHYywdpHvb4kmb215czxXHjc0aQb9jzmSCfkGErC8Wd86dgiXCw5fPxCdCv7A/XcWm7eh7bWMMnzipJAOKQySSdlYpSS/4/8U3DcO+ZH5Prl82fvP8Ou67aHrWSqm3nT2ZB9/4lJtOOzQrnz3gg/NmjuKK48Zmtfsz847NufCRUgfS/qz6cl6el07Msa8BvpvnOAuBhTm2rwIm5di+I9dnFKJNu5oJ+rJHmHsCL7fcZDy2c+zX9qq855Y4s+eb4yniKZugzyISsNoFwF5QvOyDmvQodmVpmIAlJNq0WXdP1tQJpKo30Gpde8YnZAXhXk3v8qIAm3ZFaUnY1DbGePbtzTxx5Sy2uaktv1q6Lj0C7aXJAO2ee8YOKub6RWvaBdIAPzntUPw+4cmrZlEfTdAvHOBcN0j39vvx063pOP/a0Zz3y0Wm4eURQgGLH596SLpmeebxnvrO0fzmufXM/9bhDHPzxZMpm8seWsWgkhDXnTiOcZUl7RYiunHJu+kvHpFg5kRNK2vVU+8cOlP6MNcvGy+tq+FXsydxy+xJlBUFKAn5aUmkOG/mKMqKAgwobg3AN+9q5rKHVrY9rNZHVwXhQKe+9FlesLlpVzMjyou47JjRDCwOsaup5+WeDywJUNvo5PP5LaeDNQYqSgL4gOr69tfkBfZeLfQh/Vtr0TbHk1SWhvN+Xr4Fifa2qsv+0NVKM0oVMK3WlYNXSrYk5OfBS4+kJZFiS30Ld7+6IZ3icfucw7ljzmRaEja3PPd+uq55Oqi/cDp3Z6RcZKZgeIaXRxCR9KTKzO1b6qIkUoaRFUU0ROMUhXztcuIhe7JkrjKJd86dQihgZZU3vPeCaexqilMS9udcTKglYTOoNMiFf1zB1BFl3D5ncjpI/9GpE7hxybvcOXdKznMZOaAIESiLtE7U7ErpQ6++edsg37ZNzgD8jRtPgOIvf7/WR1eFQAP1A8ALLP0WlIb8/GtnEz94ag2JlCGWzDUWXXhESOfShwN+Ar4kYgzGrU3rleSqjyY4dEgpn25vSueS+y0LAfw+cWbc17V0KaDNN9mzOydrFuI5KbWfHfBqXSJyFU7qDCNHjtyTt+5TyaTNhzUNWWkVd86dwoCiID/5xqFs2+0E1ZGgj4qSIEVBP1ccNxbbmHTKS100QdgvnD19BFccN5ZhZRHWb63nuhPHZ00OvfeCaby6biuPXD6TnU1OTviSqk18/6TxhPwWlz7YukLnXeccwZD+/g4nS9Y2xkikUlk55vNfcBZSumX2JEZVFOGzhFufX5eeSOqt5JlZJvGz7U387PSJrNvawNub6qh3FyW6+YyJ6V8Y8q1kGgn6GNJmomZXSh/mC/IjwS8PwG3b4LNolyOv9dFVodBA/QD4/f9+giVOze8tdVF2tyTTf538VvekvnQmxzyTF6QP7x/iHzd+jfPuf7NdqkdzPJkOUH++9H1qdrdgG+OmxhgGloTx+4TxlSWUFQW7FNB2parL/lKI56TUPlAw1boKoVJXMmmzuT6aDtLBGSX+4VPvOPXOd8eIxlPcMXcyPhFqG+LcuCS7lvf8Fz6gtjGWTkfxgmwbYWBJIB2U948EeHrVv/jKhMFcvHBF1uTHAcUB5i54k0EloXRud0NLklEVVrvJkl6gPbw8wn0XTsc2htqGFiyRrBH+cMAiHLD49n3tU2dumT0pPZHVyzP/3flT04G1l6/vlVoEshZL8j7jvgunEw7krgztlT7cU/mCfKDDUfrMSaiDSkLcMnsSYwYWUxTyMbBYSy+qwqCB+n7gpbl8vrOJIf3CfPRFA0agKdaa72aJM9lw0dVHc8tf17GhZjf10WR69c3+ET8tCdudhNn1UfeATzAGUrbBbwmRoI9oPIlBSNnmS4N2y12Q6DdnTWbZBzXUNcf5bEczAZ8wuDSE32elR8a90eXbXvyAj2oaCfjgoFInSE+kDDeffogGtEr1HFqty2Xbhi31UWobYjlTOppiSeqiCWckWATbkFVDvW2Othc8L7hwOgNLnEot0bjNgmWfcNa0YYQDPs6dOYqLFq7IOsZ3Hq1i0ZWzstJMMgPh8uJAuhKMAYaVhfnPc47AGEN9NM61T6xJl2q8+YyJVBQHGdI/TG1DC9GEnTddxSv36E0GDfitdGBt26ZdqUVvsaRbZk9i7KBiNtY28bO/rKW2McYDF89gwuDSfRYM5wvyOxqlz5yEWr0rmv4i8sy8YzVIVwWjUBY86jVeXbeNn/5lLdW7mjDGsHbzbhrjqXSQ7reEQSVBDioLc9hB/Tl8WH/mHX8wpeEgYwYWM+mgfowZWExpOMg1Xz2YytIwQ/uHnEWB9rDfCPiEoE8oDvo4ZEg/Dh3aj8MO6kf/ogCxpE3KOKUR2wbpfkuIBHzO6qgCI8ojHDWmgrvPmwY4tcTjKZvhZWEwUF0XJWBJ1qJCxx9SyQvXf4U/XjyDqSPKsQ17vPCQUurAcqt1LQcmiEi1Wz1rPnCyiGwATnafg1O1ZSNOta4HgHngVOsCvGpdK2lfresP7ns+IbtaV67PKCg7muLUNMTSq4lmGl4eoTTs55bn1jFnwXIuXriCRCp3vvjIiiLCAYv/+PYU/mPuFIwxzFmwnNn3vMFFC1dwyTGj0rXOa/J8KUgaw3Unjmv3ReDqR6uoa04STzkDPPXRBLc8t46PtjVw8cIVDOnvfDEYXh5JL2QUS9pc+/jbnHXvcmrdFVDbXptlwQ+feoer/1RFbWOMO+ZMxp8jdWXKiP7c5x4fnFSbyn4hbn1+HZc9tDJdmvHKR1a1W1V1f+hogaJ85TV1EqkqJDqivo+0JFLsbklw16sbaI4naYw5q1F6BBhQHGBAcZCkbUjZMM+tbZsrv/nosQNYvnEndc1xmuIpUrbBZ7UmivstJze8pYMc96DPIpGy0x02wI6mGA0tyXS1llwEZ3QEhAmVxbx4w1fTr7WtF94vEqQ5nqS8OJQzANd0EKV6Dq3W1bF4MsWOpjirP9vBvRdMY95jq7PSUea/sD4d+A0qCWEbkzNH+uOaRgCufWIN8791ONc8ll3XvKYhzs3Pru0wz3t7Q4yRFUU5A83K0hA/yrMg0eZdURav3MRDl80k4HP+Ctz6fGsVmnDA4q5zjuD6J9e0jtJfNJ3fv/ZJVvnE2190Ul8yJ2V6pRbLItkLEtm2nV7sKPM8uzsg1kmkqifQQL0LjDE0xpLsbkmyvaGFpe9sYe3mejJTzouCPsqLAjTFkhw8qJSt9dGcedmZAa03+TSRShGNp7AELJ9QURzki90x/BYEfF/+Y8jAkhCb66L43cUqookUO5sSlBcFCAd8fL6jOWeuetI2hAJCWSjATacdmvWa1gtXSvVVQb+PJVWb+OnpE7n1+XVZgasIWcHod44/mPkvrG+Xo33vBdP4xbPvc9Nph1C9K8qQ/uF2wXZR0Ndhnvedc6dw6/Pr+eEp43MGmlvqojx46ZHURxPtFiTa0RRncVU1i6uqGV4eYen3juUX3zwsXUP8V0vXAXDzGRMZX1nCZzuaGVQS4p8bd7C4qjrrc/IFtG3TULxR+kILiLtSaUapA0UD9b2QTNnsbknS0JJgc12Up6uq+dt7W7NyyfuF/W5A7CeeSjFyQP9Or1B53+sbCfiEHY1JLEucXEfb0NCSdD7fhoDbv+WbFBryW/h9QllRgEElIeqjCYaXF1HXHGdgibNSnM8SbGMwxjnOoJIgTfEULUmbqSPKc07y1HrhSqm+wLYNO5riWbnNFcVBbjh5ArYxvLSuJiswv++i6VnBaFkkwEvraqhtiGcF9OVFAWobY+mR8lxL2jfHUznzvEcMiBAO+Pj1X9/n7U11LF2zhXvOn8Z3H1/dbvQc4EenTsiaLJr5Gjij2tG4c32Zq5WCs5CRN9n1jRtP6FJAW6gBcVcqzSh1oGigvgdaEinqowma4yn+XFXNn978PF3yCpxRkBkjy/lgWwORgEVxyO/kgtvsUflBb9Q6nrKddBecXPF4yibkt4glbaeaioDPcgJ3wXlsiWCA0pCPytIwN58+MSvYzqzWEvJbTsUZ45ROHNw/kq5pnu9LRWa98GTKZtvuGAnbJmAJyz6o0TQXpVSPl2tJem/y44TBpWypj7YLrpdUbcoq8ecF229vqsta0OjRK2by+JWziMaTLLhwOtsb41lVWk6ZWMmYgcX86YqZfLa9md++soHaxhhBv8XtL37AL795GJccMwaA2VOHcc9rG9ITQgeVhnht/RfpMopegH9wZQl+S/jl0rVZiyZ5o9q5AmkvqPf2mTA4stcBbSEHxHtbaUapA0WM6ZYKVwVnxowZZtWqVe2227ahIZZkdzRBPJli1ee7eOD1jXxc25TexydQFPLzw5PGc+rhQ3hvUz0P/vMzqnc1UxLyOyky8RQjOlmK0Aumv6hvIZ6ySdkmqxpMYyyFT4SkbeO3LII+YVh5EY2xZLu0mrYLLR09dgBPr95MwCckUzab61oApyqAV7nlyyZ8LvugJqOiS3bVF50sqtSBJyJVxpgZ3X0eB1K+PntvtB0991lw5u/eaJeq4S0pv7MpxodfNLQrgTh6YBHvVu+mLBLANgZLJD1S7a1aWlYU4NHlnzFtdAWVpSH6RwL4LNhY28zAkiAGsnLff3/BNBpjSR5841O+f9J4mmMpHvjHJ+1WDPXOcdFVszj3/jezvmCMG1TCzmic5liKT7c3pYP/zMortm3Y3hTrcB+l1L6xJ322Buqutp1+PGmzuyVBY0uSWDLFax/WsnjVJjZmBOhBn0V5UYB+YT/xlM2QfhGe/M7R6de9XPOAT7IW9+lMIPzzpe/T2JJgZ3PriL0lICKcOXkIX+yOf2kd8nyfP2faMJZv3Jn1RaIpntqjmub56qh3NBqvlNo/NFDfe7lGz++7cDo/+0v26DM4K1oOKy/Ctg2f7Wji8x3NFAV9NMdTjKoooiTs51v3/jMdPE8dUcZ1J47j4EHFxFOGaDxJS8ImHLC4JiMYv3PuFEZWFJFMGc574M12wfeTV81CxEl0/HxHc3qO0pwFy9tdz//++Hj8lpAyEA5YDIgE2VDb2O76hpaFKYu0H9XOlfKjQbpS+9ae9Nma+tJGUyzJ7pYE0XiKpliS597dypLV1WxvbC0jFXRX2CwO+vH5LHyWEAr42FKfPSHIyzX3gtmioJ/meJL7Xt/YYTDsVYG5btHbWSUZIwEfpWE/X+yOdyoYzvf5yzfu7HIwrZNKlVK9QWYtbWgtcegt8OPJnPxoWcLoimJKw4F0QFseCbAzGufRK47KGpEeMSDCtt0t3LDYGVl/8NIjuWHx2naLJT1x5Sy21EVzVnFJpAwX/rF1lHzhpTOIBHKvQBrwWRxU1lpesbYhlvP68tUK11QQpQqLBuqulG3YtLOZRMqmtiHGM29v5q/vbKEp7pSPsgS+On4Q5xw5ggXLNrKrOUYw4MMSp6PzVuXM1JVg9vhDKukXCTByQJE7kuIwxnQ6GN6fwbROKlVK9Qb5ammPGVicDoRzTX7MDGhzjcr//oJptCRsQn5fOkiH7IoumZ+XSNl5SzF+ur0pK9C+/KFV3HP+1JwlIitLsoNsrRWuVM+mgboraRs+2tbA4lWbeGV9jTPJEgj7Lb5x+FDOnj6Mof0jlIT9fO+Eg/n18+tpSaSyUkraThjtajDb3e/vSOak0o7aQCmlClm+WtpFIV+nJz/mGpW/5rHV3HzGRETIOna+YHxLXZQFyz7JmliamYaTqXpXlJaEzZKqah689EjnV12/xeDSMH5/dulerRWuVM+mK5O6Nu+KcsXDq/j7+9tI2obyogCXHzuaRVfN4toTxzFucCnDy4uoLA1z0mFD+PWZh1FZGqY+msi74ubVXxlLImVojicxxrnfk2C2u9/fkeMPqexUGyilVCHzKp54K2l6o+cDi0N5V7RsK9+odVkk0G4VUy8Yz/y8//r2FO586SNqG2MM7hfmz/OO4Y0bT+CZeccytCxMbWMs69jDyyPURRMsrqrm5P96nYsXriDo97UL0ju6vu4ujaiU6hydTOoKDR1nhl5yF8PLI3x7xnBOmTiEUMBHSchPWVGgUwsM5eJVXfmyiZ+F+n6lVOHTyaRfrqNJkl2dQFnbEOOse9tXibn5jIksqdrE908az9V/qkqPkj9y+UxKwn4SSZuA38JvSbqeeXkkwC63ypj3vO1k0AUXTue3r3zES+tqskpH5jvnzOtr+3k6WVSpA0+rvuyF/iMPMXcvepFj/q0Cn2VRGvZTFgng38sAXSmlDhQN1DvWUV30fVH1JNfxbzt7Mg//81NuOHkC4waVZAXf+Y6Z7zzbvr9tMN/ZYHtP2kEptf9ooL4XDj9imln6P//QAF0p1eNooN6xfCPeXl10T1cCWa8OeTTuzNeJxpNsb4wzqqKI0RXFnQqEO3uee2t/H18p1TlanhEQka8D/w34gD8YY+Z3tL/PEkaURzRAV0qpXqazlU9yTQq98pFVnQpkLUsQhAv+8NZeB8L7u0KLVoBRqufplVGpiPiAe4DTgInAeSIysaP3+C3RIF0ppXohr/JJplyVT7oayHb1/Z09z721v4+vlNr3emtkOhP42Biz0RgTBxYBs7v5nJRSSnWDzlY+6Wog29X37+8KLVoBRqmep7emvgwDNmU8rwaO6qZzUUop1Y0sS5gwuPRL66J7gWzbHPXOBrJdfX9nz29JfgoAAAilSURBVHNv7e/jK6X2vd4aqOfqddrNmhWRq4CrAEaOHLm/z0kppVQ3yVxJtKN9uhLI7otAuDPn2RX7+/hKqX2rtwbq1cCIjOfDgS1tdzLG3A/cD04FgQNzakoppQpVVwNZDYSVUvtSb81RXwmME5ExIhIEzgWWdvM5KaWUUkop1Wm9MlA3xiSB7wF/B9YDi40x73fvWSmllMpHRL4uIh+KyMciclN3n49SShWC3pr6gjHmb8Dfuvs8lFJKdSyjpO7JOKmLK0VkqTFmXfeemVJKda9eOaKulFKqR9GSukoplYMG6koppbpbrpK6w9ruJCJXicgqEVlVW1t7wE5OKaW6S69NfdlTVVVV20Xk8+4+jwNgILC9u0+iQGhbtNK2cPTUdhjV3SfQRZ0qqZtZqUtEarXP7nO0LVppW7TqiW3R6T5bA3WXMWZQd5/DgSAiq4wxM7r7PAqBtkUrbQuHtkO36VRJ3UzaZ/c92hattC1a9fa20NQXpZRS3U1L6iqlVA46oq6UUqpbGWOSIuKV1PUBC7WkrlJKaaDeF93f3SdQQLQtWmlbOLQduomW1M1L/0220rZopW3Rqle3hRjTbr6OUkoppZRSqptpjrpSSimllFIFSAP1XkBEFopIjYiszdg2QEReFpEN7n25u11E5LfuMt3visi0jPdc4u6/QUQu6Y5r6QoRGSEir4nIehF5X0S+727vi20RFpEVIvKO2xa/crePEZG33Ot60p24h4iE3Ocfu6+PzjjWT9ztH4rIqd1zRV0jIj4ReVtEnnOf98l2UIVB+2yH9tmttM9uT/ttlzFGbz38BnwFmAaszdh2O3CT+/gm4Db38TeAF3DqFs8C3nK3DwA2uvfl7uPy7r62PWyHocA093Ep8BEwsY+2hQAl7uMA8JZ7jYuBc93tC4Br3MfzgAXu43OBJ93HE4F3gBAwBvgE8HX39e1Fe/wAeBx4zn3eJ9tBb4Vx0z47fc3aZ7e2hfbZ7dtE+21jdES9NzDGvA7sbLN5NvCw+/hh4N8ztj9iHG8CZSIyFDgVeNkYs9MYswt4Gfj6/j/7fccYs9UYs9p93ACsx1ndsC+2hTHGNLpPA+7NAF8Dnna3t20Lr42eBk4UEXG3LzLGxIwxnwIf4yz33mOIyHDgdOAP7nOhD7aDKhzaZzu0z26lfXY27bdbaaDeew02xmwFpzMEKt3t+Zbq7tQS3j2F+9PXVJxRiT7ZFu7PhmuAGpw/XJ8AdcaYpLtL5nWlr9l9vR6ooHe0xV3A/wVs93kFfbMdVGHrk/2UR/ts7bPb0H7bpYF635Nvqe5OLeHdE4hICbAEuN4Ys7ujXXNs6zVtYYxJGWOOwFnlcSZwaK7d3Pte2RYicgZQY4ypytycY9de3Q6qR+v1/ya1z3Zon+3QfjubBuq91zb3J0Hc+xp3e76luvd4Ce9CJCIBnA7/MWPMn93NfbItPMaYOmAZTr5jmYh46ydkXlf6mt3X++P8NN/T2+JY4EwR+QxYhPPT6V30vXZQha9P9lPaZ7fXx/ts0H47iwbqvddSwJv5fgnwbMb2i93Z87OAevenxb8Dp4hIuTvD/hR3W4/h5qT9EVhvjPnPjJf6YlsMEpEy93EEOAkn//M1YI67W9u28NpoDvCqMca42891Z9WPAcYBKw7MVXSdMeYnxpjhxpjROJOMXjXGXEAfawfVI/TFfkr7bJf22a20325jf89W1dv+vwFPAFuBBM43yCtw8rNeATa49wPcfQW4Byf37T1gRsZxLseZbPExcFl3X9detMNxOD9rvQuscW/f6KNtMRl4222LtcDP3e1jcTqqj4GngJC7Pew+/9h9fWzGsX7qttGHwGndfW1daJPjaa0e0GfbQW/df9M+O33+2me3nr/22bnbpc/327oyqVJKKaWUUgVIU1+UUkoppZQqQBqoK6WUUkopVYA0UFdKKaWUUqoAaaCulFJKKaVUAdJAXSmllFJKqQKkgbpSLhEZLCKPi8hGEakSkeUiclaO/UaLyNoc238tIid14nOmiogRkVP31bkrpVRfo3226gs0UFeK9MIbfwFeN8aMNcZMx1loYXib/fy53g9gjPm5MeZ/OvFx5wH/z73PeS4iov83lVIqD+2zVV+h/7CUcnwNiBtjFngbjDGfG2PuFpFLReQpEfkr8FK+A4jIQyIyR0ROE5HFGduPd9/r/XGZA1yKs5Je2N0+WkTWi8i9wGpghIic4o4QrXY/v8Td9+cislJE1orI/e4xlVKqL9E+W/UJGqgr5TgMp7PN52jgEmPM1zpxrJeBWSJS7D4/B3jSfXws8Kkx5hNgGc4qfJ4JwCPGmKlAE/Az4CRjzDRgFfADd7/fGWOONMZMAiLAGZ04J6WU6k20z1Z9ggbqSuUgIveIyDsistLd9LIxZmdn3muMSQIvAt90f3Y9HXjWffk8YJH7eBHZP6V+box50308C5gIvCEia4BLgFHuayeIyFsi8h7OqNJhe36FSinVe2ifrXqrvLlbSvUx7wNne0+MMd8VkYE4oyLgjJbsiSeB7wI7gZXGmAYR8bmfcaaI/BQQoEJESnN8huD8ocnKiXR/dr0XmGGM2SQivwTCe3huSinV02mfrfoEHVFXyvEqEBaRazK2FXXheMuAacCVtP6EehLwjjFmhDFmtDFmFLAE+Pcc738TOFZE/g1ARIpEZDytHfx2N/9xThfOUSmleirts1WfoIG6UoAxxuB0vl8VkU9FZAXwMHBjnrdMEJHqjNvcNsdLAc8Bp7n34Pxk+kyb4ywBzs9xPrU4k5eeEJF3cf4IHGKMqQMeAN7DqXiwsu17lVKqt9M+W/UV4vxbV0oppZRSShUSHVFXSimllFKqAGmgrpRSSimlVAHSQF0ppZRSSqkCpIG6UkoppZRSBUgDdaWUUkoppQqQBupKKaWUUkoVIA3UlVJKKaWUKkAaqCullFJKKVWA/j8NarfxpEPm0AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,4))\n",
    "ax1 = fig.add_subplot(1,2,1)\n",
    "sns.regplot(df['GrLivArea'], df['SalePrice'])\n",
    "ax2 = fig.add_subplot(1,2,2)\n",
    "sns.scatterplot(df['GrLivArea'], df['SalePrice'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 画出与 SalePrice 相关系数较高的前几个特征与 SalePrice 的散点分布图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1d1c61bb048>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df[:len(train)].loc[:,['SalePrice', 'OverallQual', 'GrLivArea', 'GarageArea', 'TotalBsmtSF']].fillna(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OverallQual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d1c8ff3710>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2W6/3tnWGrrP55flHMHpYrZM+VGIDt37fZVXN4xfhNMaYj6zG+gwXTd+feY+s4tQFK5j3yCoumr4/jQ7nSgB2dIWPKnZ0ucuKq8QGbgMN6b8VkZtF5H+JyNTCz0CeKCJJEXlBRB4J7k8Ukd+JyBoRua+wXkdEaoL7a4PHJxS9xneC9tUi8tmi9hlB21oRuayoPfQcxpjqtXlHjvOCCXHwP+DPu6uVzTtyTvtRvuKyuz4kEsKkxqEsOedwfn3J0Sw553AmNQ6N9DLeQIPNEcCBwA+A64KfHw3wuV8HXi26fzVwvapOAtqBs4L2s4B2Vd0PuD44DhE5ADgtOP8M4JYggCWBnwCfAw4Avhwc29c5jDFVqjMGK/cB6jLJ0IrLdZnoLmH1VJg3OuW2Z/nba5/mlNueZc2GrZFWUxho6vNndufFRWQc8AXgSuBb4i+RPQb4SnDIYuAKYD4wM7gNftHPm4PjZwL3qmon8IaIrAU+FRy3VlVfD851LzBTRF7t4xzGmColEDpn49rwugwTRg3hnrOnkVclKUIq6be7Uon6bH0GGxE5DFgA/A/gFeBrqvpqX8/p4d+Ab7Mzm20ksFlVC+PWNmBscHss/mJRVDUnIu8Hx48FVhS9ZvFz1vVoP6yfcxhjqlRtOsFF0/fvvpQ2rqGO+bOaqE27TRDwPGXj1q5ea1wa62sHdTZaf+/yT4B/xP8A/zF+8BgQETkOWK+qrcXNIYdqP4/tqfawPs4VkRYRadmwYUPYIcaYQaIzp6FzNp05t6nP67d2dgeaQj/OvauV9Vs7nfWhkI1WLOpstP4uoyUK2woA94vId3bhtY8Eviginwdqgb3wg9VwEUkFI49xwNvB8W3AvkCbiKSAvYFNRe0Fxc8Ja3+vj3OUUNUF+CM3mpub3f6LM6aKxKG0f1feC/0235V3WxutXD9yDvvRUJdm0ZxPsm5TR/fmafuOqIu0gkB/I5vhIvK/Cz8h98tS1e+o6jhVnYA/wf+Uqp4O/Ao4KThsNvBwcHtpcJ/g8aeCraiXAqcF2WoTgUnAc8BKYFKQeZYJzrE0eE65cxhjHItLaf9kokwWmOOgl04mQvvhcr3PB51dtG/L8r2Hf8+pC1bwvYd/T/u2LB90dkV2zv7K1Szs47mqql8b0ElEjgb+UVWPE5GPA/cCI4AXgFmq2ikitcDPgUPxRzSnFU3+/zP+VtQ54Buq+ljQ/nn80VISuENVrwzaQ8/RVx+tXI0x0diTpVE+jI1bd/DW5h2cf/fzJbXRxg6vZeRQN4spAd7v6GRzR46unJIQ8BTSKWF4XYq969y8H3uydM8eKVejqnN26azlX+dp4Ong9uvszCYrPmYHcHKZ51+Jn9HWs/1R4NGQ9tBzGGPcy+byNA6tKalyfOvTf3Kecpz34Oan1pT04+an1vCvX/qfTvuRyykfbO/ivKKgN//0qQxNu0t9zmuZ0j0RDjYHui30GOCHwMdU9XPBepbDVfVn0XXNGDMY1GWSfHvGZC55YOf+La7XlQBk816ZPVzcztnsyHndgQaCRIW7n+e+udOc9aE2HV6uJsrMvIG+8iLgCeBjwf3XgG9E0SFjzOCS87Q70ID/4XrJAy+TczxnIxA6V+JauYKgLt+PUfU1oeVqRtVHdxlvoJunjVLVJYVstGAdjNsxsDHmI6krVyYLLOd2RCEC1518MBff/1L3COu6kw/GcX4AqUT4ttAphx1JJITJY4bx0PlHOssQHGiw2SYiIwnWq4jINOD9yHpljBk0KlFhOExChNp0gnkzD+pO961NJ/ALlbiTSSW45fSpvRIVMg73swE/4LhM0BhosPkWfgry/xCRZ4BGdqYWG2NMWYUKw5XcmRL8b8oX/OKFXkHv/nMPd9qPjmyeu579S8n21Lf/5nUunL6f0364NtDaaM+LyN8Ck/Evfa5W1egSso0xg0YlLtmEKXs5z/GiznQywW9f38iS1rbutnENdXzz7/d32g/X+quNVm7h5v4igqr+MoI+GWMGGdeXbMIkExJaiDPp+DJaOinMP31qr9TntMs9Biqgv5HN8X08poAFG2PMR0JNKsGFx0zqNVdS43iuJJtXbuqx3uemp9ZwxRcPctoP15ws6jTGVLc41EbbkfNCF3V+//gDnfbDUw1d7/P94wd3ecaBJgggIl/A38Csu66Dqv4gik4ZYwaPQm20ngkCk8cMcxpwEgKzj5jIpQ/uXFx69YlTnO6QCTvX+/RMVHB9Ec31F4CBVhC4FRgCfAb4KX4m2nOR9coYM2hs3Jbl+mWrS0YU1y9bzZUnTHE6j6NKd6ABPzng0gdfdrpyH/z1Pj+d3UQqkeyujZbz8k7X+1TiC8BARzZHqOoUEXlZVf9FRK7D5muMMQPgeR5nffrjvRZTep7bLLB8mZX7eceVDGpSCbpyyv+5+7mSBAGX62wqsVPnQP90hb+h7SLyMfzqyxMj6ZExZlDJK92BBvwPtovvfynSoo9hEmW2GHA9d9SZ09DaaC43cYvjTp0Fj4jIcOAaoBV4A7+EvzHG9CkOm4UB1KX9lfvF9cBuOX0qdY63hc7F4P1Ip8L31ElHOLrq85VF5JMi8leqOk9VNwNDgVeA+4HrI+uVMWbQKLdpmesRRTan/PtLb7HwzE/y1MV/y8IzP8m/v/QWWcfbQsdhE7dUQrj2pCklgffak6ZEWp+tvzmb24C/AxCRo4CrgIuAQ/C3U7aSNcaYPtWlE1x70pTeWww4HlGActTkMcxZtLIkGy0o+ehMIgE3f+VQ2rd1dddoa6hPk3D4dnRk81zzeGnSxjWPr+bmrxwK9dGcs79gk1TVTcHtU4EFqvog8KCIvBhNl4wxg03jsJqSApiVqCbgxSQbLYnQlfP43sO/7w56159yMEmHyc+ZVJLGYaW16RqHZSItjtpfLE2KSCEgTQeeKnpswGt0jDHVqzPncc3jfyQbzElk8/79TsdbDJTbndJTtyObnKd8c0lpwsQ3l7zkdD+bhro0/zB9f+Y9sopTF6xg3iOr+Ifp+9NQl47snP0Fm3uAX4vIw/gZaf8JICL70c8WAyJSKyLPichLIvIHEfmXoH2iiPxORNaIyH0ikgnaa4L7a4PHJxS91neC9tUi8tmi9hlB21oRuayoPfQcxhj3uvL+ivlzft7KqQtWcM7PW3ly1XpyjtPRklJm7shxbbSuMinYXQ6DTXtHF+fe1VoS8M69q5X2jujqK/dXruZKEVkO7AM8qdr9FSCBP3fTl07gGFXdKiJp4L9E5DH87QquV9V7g8WiZwHzg9/tqrqfiJwGXA2cGmxBfRp+9YKPAf8hIoXyqD8B/h5oA1aKyFJVXRU8N+wcxhjHyhXAdJ0gkIjBYkqIx+ZpsUx9VtUVqvqQqm4rantNVZ/v53mqqluDu+ngR4FjgAeC9sXAl4LbM4P7BI9PF39Xo5nAvaraqapvAGuBTwU/a1X1dVXN4qdizwyeU+4cxhjH6jMJLupxyeai6ftTn3GbIJBOJkiKsG7TdjZs6WTdpu0kRUgn3fZjaG2C+bOaSjLB5s9qYmitu34UNrQrFvWGdpHOu4hIEn9dzn74o5A/AZtVNRcc0gaMDW6PBdZB97bT7wMjg/YVRS9b/Jx1PdoPC55T7hzGGMc6upSblr9WWuV4+Wtc8cWDaHDYj7ynvLc1WzIxf+1JU9irNrp5ijDDamoYNdTjnrOn4amSECGV9NtdqcSGdpEGG1XNA4cEC0IfAv4m7LDgd9gYUvtoD/sa0NfxvYjIXGAuwPjx48MOMcZ8aBpaAFMcpxx3edqdfg3+ZaNLHniZex1noyUSwphhdRWtgl2JDe2cZJSp6mYReRqYBgwXkVQw8hgHvB0c1gbsC7QFGXB7A5uK2guKnxPW/l4f5+jZrwX464Vobm4e3PW9jamQuBTAzHtK49CakhHWrU//Cc9xbbS4cL2hXWTBRkQaga4g0NThLw69GvgV/mLQe4HZwMPBU5YG958NHn9KVVVElgK/EJEf4ycITMKvOC3AJBGZCLyFn0TwleA55c5hjHEsLgUw69IJvn/8AVx0zwvdI6ybvnwotY4Xl8ZlywXXonyX9wF+JSIvAyuBZar6CHAp8C0RWYs/v/Kz4PifASOD9m8BlwGo6h+AJcAq4HHgAlXNB6OWC4EngFeBJcGx9HEOY6qK5ykbtnTyVrs/KV6Jb/FxKM8CkPfoDjTgB7yL7nkBxyXaylZc3rgt67YjjkU2slHVl4FDQ9pfx88k69m+Azi5zGtdCVwZ0v4o8OhAz2FMNfE85c8bt/GXjdu7V+7/9cghTBhZ7/QbdDoh3DZrKuu3ZLv7MXpYhrTr2mhlCmB2OY42lUg7jgOrAmDMILW5I8vGrZ29yqIMH5JmRL3DcjECNenSlNqadDLa6yohyq1vcT3CEgnvhzheXOqa60p4xhhHdmTzoWVRdmQdf4NW2NaZK2na1pkDx5evKlHpOExS4LqTDy7px3UnH+x8e2rXbGRjzCBVriyKyxpc4MeU7dl8r/UtjmMNnXkvtNLxjV8+xGk/0qkEw4ekWDTnU92VDPJePtK9ZOLAgo0xg1RcLhvlYrK+JSnChq2dnPPz1u62StRGU5RN27q45IHWkuA7qgKVsF2yYGMGHc/Tii6Yi4vaTILrTzm4+1JaYc6m1nGZGK/MCMt1ZlxtKsHCM5tpa9/RnagwrqGWWscjih1ZLzT43jd3WmR7ycSBBRszqFTrGoYwqYQwvD5Tso/M8PqM8zmKOBSeBD8b7f2OXMnlvH879RCG1rj9GCy31YHjItjODe6LhKbqVOsahjBbd+SZs3Alcxat5NQFK5izaCVzFq5k6w7HCQICN552aMmE+I2nHRpeWCpCnsI37nux5N/GN+57EddLj2rT4UUwXS8udc1GNmZQqdY1DGHisnK/JpVgxNB0yYR4Kum3uxSX92NUfU1oEcxRLtPRK8CCjRlUCqXTe16yibJ0elwl45IgkFfe2byje56iMCFeN8rtBEUqWeZynuOc40oUwYwDCzZmUKlE6fS4SiaE+adP5b2tO1fujxqacR5s4lJtOSnCT89o4p33O7vfj332riE5yBdTxoUFGzOoVOu3xjCZlIBIyYT4/FlNfrtD5S5fuc5GS6WEnEfJ+3HrrCZSjt+Pak1iGdwzUqYqFUqnj20YQuOwmkH9H7gvHVmP83rsM3/eXa10ZN0up8ykEhx7wGhu+2oT982dxm1fbeLYA0Y7X8S4I+txbo/349y7Wtnh+P2o1iQWG9kYE4E4rPXJxaSCQDohXHjMJM6/+/nub/K3nD7VeSHOuLwf1ZrEYsHGmD3M85S3Nm+nM6ckBDq6PHZ05Rg7fIjTgJNKCMceMJoTm/btLs/yYOs65+tbduS87kAD/gfr+Xc/73zOJi7rfao1icUuoxmzh32wI0v79i7OXPgcx1z3a85c+Bzt27v4YIfbyyR1mQQXTd+feY+s4tQFK5j3yCoumr4/dY4rCMRlziadFG45fWrJep9bTp9K2nE2WiGJpbgf1ZDEYiMbY/awbZ15bn5qTUnBx5ufWsPlxx/I8CHu+rGjzJzNEsdlUdLJRJmUY7dBL5tX/v2lt1h45idJJoS8pzzQ8iZfPWKi035UaxKLBRtj9jARmH3ERC59cOe6kqtPnILrDNtyVZ+7HI8o6jPC/FlN3YGvkBVXn3H7hqQTwtGfGMOcRStL1vu4njuCnUks1cSCjTF7mCrdgQb8D/hLHwwKLTqULjNn4/rDdWunx03LXysZ6d20/DUuP/5A9nY40qvNCKOG1ZTUihs1rIZax0GvWlmwMWYPK19o0X2ZmIuOmcR5RVlg80+f6rxMTM5Tnly1nidXrS9p/+cvHOC0H1t2eDzz2nqOOWAfVBUR4alV7/B3B+7D3nX9P998OJEFGxHZF7gT+Cv8/ZMWqOoNIjICuA+YAPwZOEVV28XfE/UG4PPAduBMVX0+eK3ZwHeDl/5XVV0ctDcBi4A64FHg66qq5c4R1Z/VmGKpRKJMFpjbD/nOvNcdaCCYs7n7eZacU51ZYHlPueKRP3LFI38saf/M3/yV035UqyhHNjngYlV9XkSGAa0isgw4E1iuqleJyGXAZcClwOeAScHPYcB84LAgcFwONAMavM7SIHg0il7MAAAVCUlEQVTMB+YCK/CDzQzgseA1w85hTOTqaxJcMuMTtG3yP1wzSf9+fY3bYNOVL7OuxHEt+9pMgoVzPknbpo6d+8iMqHO+r046Gf4lIO04UaFaRRZsVPUd4J3g9hYReRUYC8wEjg4OWww8jR8IZgJ3qqoCK0RkuIjsExy7TFU3AQQBa4aIPA3sparPBu13Al/CDzblzmFM5DqzHu9t6ey1DfLeNSl/DO5IuUKcrrOe1INsl1fyftw2qwl1vC90TTJ8cWmN49TnauUkpIvIBOBQ4HfAmCAQFQLS6OCwscC6oqe1BW19tbeFtNPHOYyJXNZTFj7zBt877gDumzuN7x13AAufeYNsBVbuX3/KwSXrOa4/5WDnCQLZnMc5PVKwz7mrlWzObbTpKLO4tMNxP6pV5AkCIjIUeBD4hqp+IOXzP8Me0N1o35W+zcW/DMf48eN35anGlJVMwFmf/jgX379zO+brTj4Y11drMimhocdOnQ31GeeFOMulYLsuExOXxaXVKtJgIyJp/EBzt6r+Mmh+V0T2UdV3gstkhRSVNmDfoqePA94O2o/u0f500D4u5Pi+zlFCVRcACwCam5vtX5zZI9SjO9CA/4F28f0v+YspHerIepy5cGWvy2hL5k6jweGizmSZFGzXl/NqUuFzNhnH2XnVKspsNAF+Bryqqj8uemgpMBu4Kvj9cFH7hSJyL36CwPtBsHgC+KGINATHHQt8R1U3icgWEZmGf3nuDOCmfs5hTOTi8k0+Lv2ISwp2qkxBUNdZcdUqypHNkcBXgVdE5MWg7Z/wA8ASETkLeBM4OXjsUfy057X4qc9zAIKgMg9YGRz3g0KyAHAeO1OfHwt+6OMcpgpUuuJyXCbm4zKi6MprmRTsw532Iy4FQatVlNlo/0X4vArA9JDjFbigzGvdAdwR0t4CHBTSvjHsHGbwi8PGVOmEcO1JU3ptg+x6Yj4uI4pc3iuTgu12Yj7vKY1Da0oqGdz69J9szsYRqyBgBpWN27Jcv2x1yQfK9ctWc+UJU5zVovJUqcskSybm6zJJPMcVBOIyokjEZKRXm0rw7RmTe30JcB18q5UFGzOoeJ4XWgTT89x9i/YUHnv5bU5qHl9SXfgMx9WFu2IyokiVGem5nivxlO6U9MIXkYXPvMG/fLHXxRETAQs2ZlDJKyz+bekHyuLfvsEVDj9Q6jIJjjtkXEl14fmzmpzvIxOXuSMBhvQY6Q3JJMteY4+KoqFfRHZxxYTZTRZszKCSLFPe3+Ui8e1Zj9Y33uMXZ0/DUyURFHxsOHAfRjhMOc6UGVFkXI8o8N+DYgkRPMcf8nGpxl2tLNiYQSVf5gPF5TxFXTpB08RRfOX2FaUjm7TbkY1H+IjC9Xr5BNJrvspTJeF4bFNuUWfeEgScsGBjBhUtU95fHU7O7+gK3yHT9TfozpzH3Sve5OyjPt49d3T7b17nwmP2c9qPvCoX/OKF3otLXVefjsmOodXKgo0ZVMptQeyysm+uTIqt68WUtakEp08bT1v7zmrLp08b7zz7KjbVp9MJFpzRxLvvd3a/H2P2rqHW8YizWlmwMYNKMgHzT5/aa22Jyy+vtakE3z3ub/j6vS929+GG0w6h1vGHvADbs/le1addT8zHJVFhr5o0b3s7St6PW2c1sVdN2mk/qpWFdLPHeJ6yYUsnb7VvZ8OWzoosluvKK54q82YexH1zpzFv5kF4qnQ5/Bat0B1owP8W//V7X3Se85T1tDs5oNCPSx542Xn16SGZBLecPrWk+vQtp09liOPsvPaOLs7tcXnz3Ltaae/octqPamUjG7NHxGHlPviXsMLmB1yWJOnMha9vcV1SPy5VjndkPW5+ak3JZcWbn1rDFccfCA6z87K5fJm/l7y7TlQxCzZmj9i4LdsdaMD/T3z2nS08dP6Rzlbugx/0Kv0BG5fLRuW2Y066ro3mKU+uWs+Tq0qLr//zFw5w2o9MKhn6fmRSSaf9qFYWbMweEZdvjXHY7z4utdGSCeG2WVNZvyXbPSE+eljGebCJw98JwMj6DLef0dxr9D2yPuO0H9XKgo3ZI2LzrVFg4ZnNiCRIiF+iRNWj/J59EXRBYOTQ0k3LRg7NOO0D+MHGU0omxP1kCbcdGZJJMH9WU3c6eGHdkes5m0RCmDxmGA+df2TFKoJXM0sQMHtEQ12aW2c1lUwC3zqriYY6t5k+mWSi12ZYmVTCeerzgy3rGNdQR+OwGsY11PFgyzr3+9nkvNBCnF2O5462Zz1uWv5ayTbZNy1/je1Z2465mtjIxuwR7R1d/L8X21h45idLik+OOWo/p3M2AO935HptkFWfcfdPPSnCsQftU1Ib7YbTDiHpeGiTK7d5muPq03kNn7P57nFu52ziksRSrSzYmD3C8zyOmjym5APWdbVl8DPBwjbIcrl631MlmZCSy2j+JS23H/LlEhVcB72kxKMfcUliqVYWbMweEYeaZBCP1fuewoUh6deuy9UkBK4+cUqvoqQJxxfP49KPuCSxVCsLNmaPiENNMii/QZbL1fv5Mu+F65FNue0Wvn/8gU774ZXpx+WO+xGbJJYqFVmwEZE7gOOA9ap6UNA2ArgPmAD8GThFVdtFRIAbgM8D24EzVfX54Dmzge8GL/uvqro4aG8CFgF1wKPA11VVy50jqj+n8aVTidD97tOOS7R4ELpq/v5z3Y2wyl026llmP2rphDDnyIkVT8GuyyRD+1GXcfshb6nPlSVRffMUkaOArcCdRcHmGmCTql4lIpcBDap6qYh8HrgIP9gcBtygqocFgaMFaMavAtIKNAUB6jng68AK/GBzo6o+Vu4c/fW3ublZW1pa9vC7UD02b+/kzU0dvSbmx4+oY/gQd9fD39y0jaOuebpX+2++fTTjHW0m8/bm7bzx3vZel40mjhrCx4YPcdIHgPUfdLC5o4u32nd0zx2NbahleF2a0XvVOeuH5yl/3riNv2zc3t2Pvx45hAkj651PzHuesnFb1lKf9yARaVXV5v6Oi+xrp6r+BtjUo3kmsDi4vRj4UlH7nepbAQwXkX2AzwLLVHVTMDpZBswIHttLVZ9VP1re2eO1ws5hIrStMx86Mb+t0+318MKoopjryWgtumxUSPVd/Ns3cHwVja688qMnVpMNtoHO5j1+9MRqp3XiwF/fMmFkPQeN3ZtxDXUcNHbvigSaQl8ah9UwtmEIjcNqLNA45HrOZoyqvgOgqu+IyOigfSywrui4tqCtr/a2kPa+zmEi5JWdp3DbDykzGe3yClZdJsElMz5B2yb//cgk/fuut4XOlykT813HZWJg54e8qV5xSRAI+yjQ3WjftZOKzAXmAowfP35Xn26KJBPh+8i4Xq2uCr9Z/W6v9T4TRk501odcXuns8nqVsne9f0tcysQYA+4rCLwbXAIj+F34ytUG7Ft03Djg7X7ax4W093WOXlR1gao2q2pzY2Pjbv+hDAjK1SdOKakgcPWJUxDHhfXrMgmOO2Qccxat5Jjrfs2cRSs57pBxTkcVXXnlxh4r5m9c/przy1c1qfDS/q43TzMG3I9slgKzgauC3w8XtV8oIvfiJwi8H1wCewL4oYg0BMcdC3xHVTeJyBYRmQb8DjgDuKmfc5gIxSXNtrNLQ7dkvt/heh8RmH3ExN7rShwPKDpyHv/+0lu9RnlfPcLdKK/AJuZNlKnP9wBHA6NEpA24HD8ALBGRs4A3gZODwx/Fz0Rbi5/6PAcgCCrzgJXBcT9Q1ULSwXnsTH1+LPihj3OYCGWSwgWf2Y9N27qC+wku+Mx+ZJKuS7SE7yWTc1jJQMsscK3Eos6wqg6O/0qsTIwBIgw2qvrlMg9NDzlWgQvKvM4dwB0h7S3AQSHtG8POYaKVECEh0qvCsOu1JYkYrHEpnyzh9jJaXIKelYkxYFWfzR6yo0yF4R2OKwwXstF6zR05jHmJMunXrgNvuUoGecdBz8rEGLBgY/aQOOyQCfFY45JIhAc89zXJ4hH0CmVievbDysRUFws2Zo8opNkWq0Sa7ZBgjUsm2L+msMbF5UZdqlIm4LnfIfPak0qD3rUnTanYDpnF/bAyMdUnLutsPpLikmGTy3ms39pJV94jnUwwemgNKcfprZKAm79yKO3burpLkjTUpxHHX2e68rB5W7Zk7uj6Uw5m71p3m7ilytQkc/0hn04JQzLJkq0OhmSSpFOO59Fsh0yDBZvdFpcMm1zO44/vbuHcoi13b53VxCfGDHMacBIIXTmv14d8InT9bXSyeY9vLnmpZO7om0te4l6Hk+IdXXmueXx1SRr4NY+v5obTDnHWB4CGuhrer811ZwgCDKtN01DnflLeKggYu4y2m8pl2GzclnXaj/VbO7sDTaEf597VyvqtnU774SmhH/Kuy9WUnTtyOGmTSggbtnZyzs9bOXXBCs75eSsbtnY6r6YQp5pkxliw2U1xybDpypdZV5J3mwUWlz1cUslEmbkjd//U4zJXAlZ40sSHBZvdFJcMm3S5D9ek27/acplP4nydDaEf9C4/Y7N5r/syWiFB4JrHV5N1XK7GmDixYLOb4pJhM3poDbfOairpx62zmhg91O318WSZ9S2uV6t35sp80Dtc71OTToZeRqtJ2383U70sQWA3JRLCpMahLDnn8JIsMNeXKVKpBJ8YM4wl5xxOLu+RqlA2WiKRCK22vP+Y/Zz2o67og75gXEMdtWl3I85R9TWhO0KOqrcJclO9LNjsJs9T1mzYWvFsNPADXzqZQFVJJxMVuS7fUJfm+KDacnFWXEOdu5RjgBFDMtz21SbO+fnO7LzbvtrEiCHuRpyW6mtMb5FtC/1Rs6vbQm/Y0skJtzzTqwaX63pPcUnBjsv7sWlbJ+s2bWdT0XqfEfVp9h0xhBE2sjBmjxvottA2stlNcclGi0uRw7i8Hx3ZPBf84oVeQe++udOg3mlXjDFFbMZyN8UlGy0uH/JxeT/KF5902g1jTA8WbHZTXLLR4vIhH5f3ozYd/n7UWiaYMRVlczaBXZ2zgXjURovLnE2hL/Z+GFNdBjpnY8EmsDvBJi7i8CEfJ/Z+GOOOJQhUEStyWMreD2Pixy5kG2OMidygDTYiMkNEVovIWhG5rNL9McaYajYog42IJIGfAJ8DDgC+LCIHVLZXxhhTvQZlsAE+BaxV1ddVNQvcC8yscJ+MMaZqDdZgMxZYV3S/LWgrISJzRaRFRFo2bNjgrHPGGFNtBms2Wliea68cb1VdACwAEJENIvKXqDsWsVHAe5XuRIzY+7GTvRel7P3Y6cO+F389kIMGa7BpA/Ytuj8OeLuvJ6hqY6Q9ckBEWgaS714t7P3Yyd6LUvZ+7OTqvRisl9FWApNEZKKIZIDTgKUV7pMxxlStQTmyUdWciFwIPAEkgTtU9Q8V7pYxxlStQRlsAFT1UeDRSvfDsQWV7kDM2Puxk70Xpez92MnJe2G10YwxxkRusM7ZGGOMiRELNh9xIrKviPxKRF4VkT+IyNcr3ac4EJGkiLwgIo9Uui+VJiLDReQBEflj8O/k8Er3qVJE5JvB/5Pfi8g9IlJb6T65JCJ3iMh6Efl9UdsIEVkmImuC3w1RnNuCzUdfDrhYVf8GmAZcYKV5APg68GqlOxETNwCPq+ongIOp0vdFRMYC/wA0q+pB+MlDp1W2V84tAmb0aLsMWK6qk4Dlwf09zoLNR5yqvqOqzwe3t+B/kPSqllBNRGQc8AXgp5XuS6WJyF7AUcDPAFQ1q6qbK9urikoBdSKSAobQz/q7wUZVfwNs6tE8E1gc3F4MfCmKc1uwGUREZAJwKPC7yvak4v4N+DbgVbojMfBxYAOwMLis+FMRqa90pypBVd8CfgS8CbwDvK+qT1a2V7EwRlXfAf/LKzA6ipNYsBkkRGQo8CDwDVX9oNL9qRQROQ5Yr6qtle5LTKSAqcB8VT0U2EZEl0niLpiLmAlMBD4G1IvIrMr2qnpYsBkERCSNH2juVtVfVro/FXYk8EUR+TN+te9jROSuynapotqANlUtjHYfwA8+1ejvgDdUdYOqdgG/BI6ocJ/i4F0R2Qcg+L0+ipNYsPmIExHBvx7/qqr+uNL9qTRV/Y6qjlPVCfiTv0+patV+e1XV/wbWicjkoGk6sKqCXaqkN4FpIjIk+H8znSpNluhhKTA7uD0beDiKkwzaCgJV5Ejgq8ArIvJi0PZPQQUFYwAuAu4O6gS+DsypcH8qQlV/JyIPAM/jZ3G+QJVVEhCRe4CjgVEi0gZcDlwFLBGRs/AD8smRnNsqCBhjjImaXUYzxhgTOQs2xhhjImfBxhhjTOQs2BhjjImcBRtjjDGRs2BjzC4SkXEi8nBQJfdPInJDkFYc5Tm3Br8n9KjY+2kReS6o6LxaRC7YE+cxZk+zYGPMLggWA/4S+L9Bldz9gaHAlR/ydXd5zZuI/BXwC+DcoKLzkcDXROSED9MXY6JgwcaYXXMMsENVFwKoah74Jv6H/EoRObBwoIg8LSJNIlIf7COyMiiGOTN4/EwRuV9E/h/wpIgMFZHlIvK8iLxSOK4PFwCLiqp+v4dfgPSS4PUXichJRf0pjI529TzGfGhWQcCYXXMgUFLkU1U/EJE3gUeAU4DLgxpTH1PVVhH5IX7ZnK+JyHDgORH5j+DphwNTVHVTMLo5IXi9UcAKEVmq5VdeH8jO0vAFLUB/+xnt2MXzGPOh2cjGmF0jQNiHsgBPs7PUxynA/cHtY4HLgnJCTwO1wPjgsWWquqnoNX4oIi8D/4G/L9GY3ejLQP4Mu3IeYz40G9kYs2v+AJxY3BBsULYvsBLYKCJTgFOBcwqHACeq6uoezzsMv+R/welAI9Ckql1B5eq+ti3+A9CMX0ixoAl/dAN+/a9EcC4BCkkMu3oeYz40G9kYs2uWA0NE5AwAEUkC1+HPnWzH39bg28DeqvpK8JwngIuCD3xE5NAyr703/l48XSLyGeCv++nLT4AzReSQ4HVH4icqzAse/zN+8AF/H5f0bp7HmA/Ngo0xuyCY1zgBOFlE1gCv4c+B/FNwyAP4WxssKXraPPwP+peDtOV5hLsbaBaRFvzRxx/76cs7wCxggYisxt/i+EZV/XVwyO3A34rIc0DxKGqXzmPMnmBVn40ZJII1NucCR6lqe6X7Y0wxCzbGGGMiZ5fRjDHGRM6CjTHGmMhZsDHGGBM5CzbGGGMiZ8HGGGNM5CzYGGOMiZwFG2OMMZH7/zCTytcVKLcZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns.scatterplot(df['LotFrontage'].fillna(0), df['SalePrice'])\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot()\n",
    "sns.scatterplot(df['OverallQual'], df['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d1c8cdf390>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(1,1,1)\n",
    "sns.barplot(df['GarageCars'], df['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.21890159885169574"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['GarageArea'].fillna(0))\n",
    "df['GarageArea'].fillna(df['GarageArea'].mean()).skew()\n",
    "# df[df['GarageArea'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d1c6191780>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['TotalBsmtSF'].fillna(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1stFlrSF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03037379728343659"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uk9YQflBnusJ9VGbG+RUFHGjqpLN3wOtyZIpQuMukDT2FWZavbpkzuaCygKCDnXW6epf4ULjLpDW09zItJ52MwNSdU2YsM/IzKc3NUNeMxI3CXSbteHsP03Uz9azMjAsqCzjcfIr27n6vy5EpQOEuk9I7MMiJzl6mF6i/fSznVxbggF317V6XIlOAwl0m5UDjKYJON1MjUZqbQX5mgEPNp7wuRaYAhbtMyt7wotAaBjk2M6O6JIcjJ06hSVMl1hTuMimvHe/Ab0ZJrvrcI1FdnEN7zwAtXep3l9hSuMuk7G3ooDQvA79vtNUYZaTq8Lqqh0+oa0ZiS+Euk7LneIfGt49DWX4GWWl+DqvfXWJM4S4T1tHTT21rt/rbx8FnxuzibA6f6PK6FElxCneZsKGbqRopMz7VxTk0d/ZqKgKJKYW7TNie46EFOnTlPj7VxdkA6pqRmFK4y4TtOd5OTrqfguw0r0tJKuVFWaT5jSO6qSoxpHCXCdvT0MGCGXn4TCNlxiPg81FZpH53iS2Fu0yIc449xztYqAWxJ6S6OIe61m56+7U6k8SGwl0m5Hh7Dy1d/Zw3M9/rUpJSdUk2Djh6UlfvEhsKd5mQXXWhya8WlyvcJ2LWtGwMOKJwlxiJKNzN7CYz22Nm+83s82do8xdmtsvMdprZI9EtUxLNzrp2zOBcXblPSEbAT2leBnWt3V6XIikqMFYDM/MD9wNvAWqAjWa22jm3a1ib+cAXgMudcy1mVhargiUx7Kxro7o4h9yMMf8IyRlUFGZxoKnT6zIkRUVy5X4xsN85d9A51wc8Btw6os2Hgfudcy0AzrnG6JYpiWZnXTuL1CUzKeWFWbT3DNDY0eN1KZKCIgn3CuDYsNc14feGWwAsMLMXzWy9md0UrQIl8bR191PT0s0idclMSnlhFhD6h1Ik2iIJ99EGMY+cjDoAzAeuBu4AHjKzwjfsyOxeM9tkZpuamprGW6skCN1MjY6Z4dWrdtZqXVWJvkjCvQaoGva6Eqgbpc0vnXP9zrlDwB5CYf86zrkHnHMrnHMrSktLJ1qzeGxnXSiMFpcXeFxJcstM81Ock86OWl25S/RFEu4bgflmNsfM0oHbgdUj2jwJXANgZiWEumkORrNQSRy76tspzcugVItiT1p5YRY76nTlLtE3Zrg75waATwBrgd3A4865nWb2FTNbFW62FjhhZruA54C/cc6diFXR4q1dde3qkomSisIsalq6ae3q87oUSTERjWNzzq0B1ox470vDfnbAX4d/SYp6ZMNR+geD7G3oYEZ+Jo9sOOp1SUlv+E3Vy+eVeFyNpBI9oSrj0tDeQ9DBzHAoyeSUh2+q7tBNVYkyhbuMS31baEz2UCjJ5GRnBKgozGKHhkNKlCncZVzqWrvJCPgoykn3upSUsaQiX8MhJeoU7jIu9W09zCzI1BzuUbSkvICDzafo6On3uhRJIQp3iVjQOY639TCzQP3t0bSkIvS8wO76Do8rkVSicJeINXX00jcYpEI3U6NqcUVoWOl2dc1IFCncJWI1LaHpaSuLFO7RVJaXSVlehvrdJaoU7hKxmpYuMgI+SvRkatQtKs9nV71GzEj0KNwlYjUt3VQUZelmagwsLs9nf2MnPVpTVaJE4S4R6ekfpL6tm6qibK9LSUmLywsYCDr2NWjxDokOhbtEZFd9O0Gn/vZYGZobf6cmEZMoUbhLRLYdawWgUlfuMTFrWja5GQH1u0vUKNwlItuOtZKfGaAgK83rUlKSz2ecNzNPqzJJ1CjcJSKv1rRRoav2mFpcXsDu+naCwZELnYmMn8JdxtTW1c/B5lNUqb89phbNzKerb5DDJ055XYqkAIW7jOnVWvW3x8Oi8AIo6neXaFC4y5iGbqZq2oHYmj89l4DP1O8uUaFwlzFtPdbG3NIcstL9XpeS0jICfuZP101ViQ6Fu5yVc46tx1pZVlnodSlTwqKZ+exSuEsUKNzlrOraemju7OWCygKvS5kSFpfn09zZS2N7j9elSJJTuMtZbTx0EoAV1dM8rmRqGLqpulM3VWWSFO5yVq8cPkleRoDzwo/HS2ydHjGjrhmZJIW7nNUrh06yvLoIv08zQcZDfmYas6Zls71Gc8zI5Cjc5YxOnupjf2Mnb1KXTFwtrSpkW02r12VIkoso3M3sJjPbY2b7zezzZ2l3m5k5M1sRvRLFKxsPh/rbL56jcI+nZVWF1Lf10KCbqjIJY4a7mfmB+4GbgUXAHWa2aJR2ecCngA3RLlK8sfHQSdIDPo2UibNlVaH/31uP6epdJi4QQZuLgf3OuYMAZvYYcCuwa0S7rwL/DHw2qhVK3D2y4SgAz+w4TnlBJj/bXOtxRalt6P/3kP7BID4LvX/j4hkeVSXJLpJumQrg2LDXNeH3TjOzC4Eq59yvzrYjM7vXzDaZ2aampqZxFyvx0zsQWnmpuiTH61KmnDS/j5kFWRw72eV1KZLEIgn30YZJnJ6T1Mx8wH3AZ8bakXPuAefcCufcitLS0sirlLg7erKLoIPqYoW7FyqLsqht7WZQ0//KBEUS7jVA1bDXlUDdsNd5wBLgeTM7DFwKrNZN1eR2uLkLI7RCkMRfVVE2vQNBDjRpTVWZmEjCfSMw38zmmFk6cDuwemijc67NOVfinKt2zlUD64FVzrlNMalY4uLwiVPMLMwkM02ThXmhclpoBs6tR3VTVSZmzHB3zg0AnwDWAruBx51zO83sK2a2KtYFSvwNDAY5drKLOeqS8UxJbgaZaT62ary7TFAko2Vwzq0B1ox470tnaHv15MsSLx1r6WYg6HQz1UM+MyqLsnXlLhOmJ1TlDfY2dOAzOKc01+tSprTKoiz2NHTQ3TfodSmShBTu8gZ7GzqYNS1H/e0eqyrKZjDo2FGneWZk/BTu8jqN7T3Ut/WwYLqu2r1WFR6ppK4ZmQiFu7zO83tDD5ctmJ7ncSWSmxGgsiiLLcdavC5FkpDCXV7n93uayMsMMLMg0+tShNCkbS8fOEFQDzPJOCnc5bSBwSDr9jWxoCwPM83fngiunF9CS1c/u7Qyk4yTwl1O23qslfaeARbMUJdMorh8XgkA6/Y1e1yJJBuFu5z2/J4m/D5jnoZAJoyyvEzOnZHHC/s10Z6Mj8JdTnt+byMXzSokK11DIBPJFfNK2Hi4hZ5+jXeXyCncBYCmjl521LZz9cIyr0uREa6YX0LfQJBXDp30uhRJIgp3AWDtzuMAXKNwTziXzCkm3e/jhf3qd5fIKdwFgNVb65hXlst5M3UzNdFkpftZPrtIN1VlXBTuQk1LF68cPsnblpVrCGSCumJ+Cbvr22nq6PW6FEkSCndh9bbQ2iu3LqsYo6V45cr5oSGRLx3Q1btERuEurN5ax0WzCk/PZSKJZ3F5AYXZaeqakYgp3Ke4146389rxDt52oa7aE5nfZ1w5v5TfvdZI/2DQ63IkCSjcp7hfbq3D7zNuOX+m16XIGFYtLefkqT6NmpGIKNynsGDQsXprHVfOL6EkN8PrcmQMVy0opSArjdVb68ZuLFOewn0K23j4JLWt3dy6rNzrUiQC6QEft5w/g7U7j9PVN+B1OZLgFO5T2A9fOkx+ZoAbF8/wuhSJ0KqlFXT1DfLs7kavS5EEp3Cfoo6e6GLtzuPceelsstMjWiddEsDFc6YxIz+T1VtrvS5FEpzCfYr6wUuH8Jnx/pXVXpci4+D3GX++dCbP72mi5VSf1+VIAlO4T0Ft3f08vvEYq5aWM0MrLiWdW5dVMBB0PLPjuNelSAKLKNzN7CYz22Nm+83s86Ns/2sz22Vmr5rZb81sdvRLlWh57JWjnOob5INXzPG6FJmAxeX5zC3N4ckt6pqRMxsz3M3MD9wP3AwsAu4ws0Ujmm0BVjjnLgCeAP452oVKdPQPBvnhS4dZObeYJRUFXpcjE2BmvGt5Fa8cPsluLb8nZxDJnbSLgf3OuYMAZvYYcCuwa6iBc+65Ye3XA3dFs0iJntVb66hv6+Frb1/idSkSgUc2HB31/TS/keY3vvjkDt5xUeXp999zyax4lSYJLpJwrwCODXtdA1xylvYfAp6ZTFESPcPDoW8gyH3P7qWiMIurF2je9mSWnR5gWVURW462cOPiGeRkaMSTvF4kfe6jzQHrRm1odhewAvj6Gbbfa2abzGxTU5PWhIy3F/Y309bdzy3nz8Tn09S+ye6yc4oZCDo2HtYKTfJGkYR7DVA17HUl8Ibnn83seuBvgVXOuVEnnXbOPeCcW+GcW1FaWjqRemWC2nv6+cPeJhaX5zOnJMfrciQKpudnMq80l/UHTzAYHPV6S6awSMJ9IzDfzOaYWTpwO7B6eAMzuxD4LqFg16NzCejZXQ0MBh036WnUlHLZOcW09wywo67N61IkwYwZ7s65AeATwFpgN/C4c26nmX3FzFaFm30dyAV+amZbzWz1GXYnHqhr7WbzkRZWnlNMsSYISykLZuRRnJPOS/ubcU5X7/InEd2Fcc6tAdaMeO9Lw36+Psp1SZT0Dgzyk03HyE73a/HrFOQz4/J5JazeVseBplNelyMJRLfYk8yZhsaNNgTOOccvt9bR3NHLBy6fQ1a6f8z9SPJZMbuI5/c08tvdDXzxredpHVwBNP1ASnvklaNsPdbKdeeVMa8s1+tyJEYCfh9XLyzjyMkuXtx/wutyJEEo3FPUH4+28PerdzG/LJer1R2T8lbMLqIgK41/e3av+t4FULinpKe21fGeB9dTlp/BX6yowqev6Skv4Pdx1YJSNh1p0dW7AAr3lBIMOr6+9jU++egWlpQX8IuPXa4nF6eQFbOLmFmQyX26ehcU7ikh6By/3lHP27/1Ivc/d4A7Lq7ikQ9fSmmehj1OJQG/j09eO5/NR1p4enu91+WIx3RZl8T6BoJsPdbKC/ubae7sZda0bP7lXUt5x0UVGjExRb37TVU8vP4IX3t6N9csLNM3tylMZz4JtZzq46UDzWw+2kJPf5Dygkz+844LuXnJDAJ+fRmbyvw+46tvW8w7v/0y9z+3n8/ddK7XJYlHFO5JpKd/kN++1sDv9zQRdI4lFQWsnFvMrGnZ/PnScq/LkwSxfPY03nlRJQ+uO8htyyuZW6phsFORLvOSxEsHmrn539fx292NnDczn7+58Vxuf9MsZhfnqAtG3uB/37yQzICfLz+1SzdXpyiFe4JzzvHQuoPc9dAGnHN84PJq7rh4FgVZaV6XJgmsLC+Tv75hAX/Y28QPXjzsdTniAYV7AusbCPL5n23nH57ezY2LZ7Dmf13J/LI8r8uSJPH+ldXcsGg6X1uzm/UHNfZ9qlGfe4Lq7B3gnv/ayPqDJ/nktfP49PULtMCGjGnknEGXzi1my9FWPv7ff+SpT15BeWGWR5VJvOnKPQG1dfVz50Mb2HS4hfvevZTP3LBQwS4Tkpnm565LZ9M7EOQjD2+mvaff65IkThTuCeZEZy93PLie3XXtfOvOi3j7hZVj/yaRsyjNy+Df3r2MXXXtvP3+FznUrKmBpwJ1y3ho5Ffo9p5+vvfCIVpO9fG9u9/EVQu0FKFEx/WLpvPwPZfw0Yc3c+s3X+Cb77mINy8oPevUz6NNIy3JQ+GeIFq7+vjeC4fo6Bng7suqqW3pHtec65qfXcZy6dxiVn/iCj78o03c/YNXeNfyKqpLcjTyKkUp3BPAyVN9PPTCQbr7Bvng5dXMKtYC1hIbVdOy+dlHL+Nf/2cvP375CEHnuOycYq6YX0qupipIKepz91h9WzcP/OEAvf1B7rliroJdYi4nI8AX37qI337mKs6vKGDdvma+vvY1Vm+r5eSpPq/LkyjRP9Ue2nO8g0c3HiUz4OPDV85lRkGm1yXJFFI1LZt3rajiqoWlrNvXzMZDLbxy6CTLqoq49lwt8JLsFO4eeXj9EX68/jDT8zN538pq9XuKZ8ryMnnnRZVcf950XtjXxIZDJ9l6rIXa1i4+ee18jY1PUubVvBMrVqxwmzZt8uSzvXTyVB9ffHIHT2+vZ+H0PG6/uIqMgH/s3ygSJ+3d/Ty/t4k/HmnBDO6+vJqPXTWPgmxdgCQCM9vsnFsxZjuFe/ys3Xmcv/3Fdtq6+/mr6xeQn5mGXw8nSYJ684IS/vV/9vKLLbXkZQT4y6vO4X0rZ5PRZYsCAAAJXElEQVSXqZD3ksI9QTjn+PundvHcnkaOnOhiZkEmty2vZGaBvupKYhsa5767vp2vr93D715rJD8zwAevmMP7V1ZTlJPucYVTU1TD3cxuAv4d8AMPOef+ccT2DOBHwHLgBPBu59zhs+0z1cO9pqWL3+xs4OdbathR205BVhpXzi/h4jnTCPg0SEmST21LN7/b08ju+nbS/T7esmg6t62o5Mp5JVokJo4iDfcxb6iamR+4H3gLUANsNLPVzrldw5p9CGhxzs0zs9uBfwLePbHSk8+p3gGOnOhiZ10bO+va2Xj4JDvr2gE4d0Ye77iwgmWzChXqktQqirJ476WzWT67iMc2HuXJLbU8vb2evMwAl8wpZuU5xVw4q5BzSnLVP58AxrxyN7OVwJedczeGX38BwDn3/4a1WRtu87KZBYDjQKk7y85jceXunGMw6BgIOvoHg/QOhH/1D9LdP0hPf5Ce/kF6+gfD2wbpH3D0DQYZGAwyEPzT7//Tf4P0Dzr6wvvq6R+ko6ef9p4BWrv6qG/roaNn4HQN2el+lpQXcN15ZdyweAZzSnL09KiklKHumr6BIL97rZHf723k5QMnOHyi63Sb4px0KouyKMpJZ1pOOvmZaWSn+8lK85OV7icj4CMj4Cc94CPN7yPNb6T5ffh8ht8Mn4EjtPi7czDoHMGgIzgsUQwI+I10v4+0gI90v4/MtNC+h/Yb+q/hMzt9f8s5cDiCQU7/3e8fdPQOhHOhP0jfYOjngUF3ugaHw+/zkeYzAn5f6BjSQp+bkeYnPfx56X4fAb8R8FlMFtKJ2pU7UAEcG/a6BrjkTG2ccwNm1gYUA82RlRu5X++o569+spXQZ4X+ADgXOumDwejfP/BZaF3KgM9HwGcE/MbMgizyMgNUF+ewcm4xMwqyqCzK4mDTKYpz0/GFT+jLB07w8gHNoy2pZeTFyvkVhZxfUXj6Yqe5s5fmzl7auvtpbuikq2+A7v5B+gaCxOCvaEIbyg8zw4ChrP/wlXP5zA0LY/rZkYT7aP/0jDxFkbTBzO4F7g2/7DSzPcM2lxCDfwxiYeuZNyXNMZxFsh9DstcPOoZEEbNj+Gz41wTNjqRRJOFeA1QNe10J1J2hTU24W6YAODlyR865B4AHRvsQM9sUyVeNRKZj8F6y1w86hkSR7McQyR2+jcB8M5tjZunA7cDqEW1WA+8P/3wb8Luz9beLiEhsjXnlHu5D/wSwltBQyO8753aa2VeATc651cD3gB+b2X5CV+y3x7JoERE5u4jmlnHOrQHWjHjvS8N+7gHeNclaRu2uSTI6Bu8le/2gY0gUSX0Mnj2hKiIisaOnakREUlDcw93MDpvZdjPbamZveIrJzK42s7bw9q1m9qXR9uMlMys0syfM7DUz2x1+0Gv4djOz/zCz/Wb2qpld5FWto4mg/oQ+B2a2cFhtW82s3cz+akSbRD8HkRxDQp8HADP7tJntNLMdZvaomWWO2J5hZj8Jn4cNZlbtTaVnFsEx3G1mTcPOwz1e1Touzrm4/gIOAyVn2X418Kt41zXOY/gv4J7wz+lA4YjttwDPEBr/fymwweuax1l/wp+DYbX6CT0RPTuZzkGEx5DQ54HQw4uHgKzw68eBu0e0+RjwnfDPtwM/8bruCRzD3cA3va51vL/ULTNOZpYPvJnQCCGcc33OudYRzW4FfuRC1gOFZjYzzqWOKsL6k8l1wAHn3JER7yfsORjFmY4hGQSArPDzLdm88RmYWwldTAA8AVxnsXgmf3LGOoak5EW4O+A3ZrY5/MTqaFaa2TYze8bMFsezuAjMBZqAH5jZFjN7yMxGLnw62pQNFfEqcAyR1A+JfQ6Gux14dJT3E/kcjHSmY4AEPg/OuVrgG8BRoB5oc879ZkSz101NAgxNTZIQIjwGgHeGu/eeMLOqUbYnHC/C/XLn3EXAzcDHzezNI7b/kdDX06XAfwJPxrvAMQSAi4BvO+cuBE4Bnx/RJqLpGDwSSf2Jfg4ACD9Utwr46WibR3kvUc7BaWMcQ0KfBzMrInRlPgcoB3LM7K6RzUb5rQlzHiI8hqeAaufcBcCz/OmbSEKLe7g75+rC/20EfgFcPGJ7u3OuM/zzGiDNzEriXedZ1AA1zrkN4ddPEArLkW3GmrLBK2PWnwTnYMjNwB+dcw2jbEvkczDcGY8hCc7D9cAh51yTc64f+Dlw2Yg2p8+DnWVqEg+NeQzOuRPOud7wywcJrVuR8OIa7maWY2Z5Qz8DNwA7RrSZMdQnZ2YXh2tMmKkVnXPHgWNmNjSl23XArhHNVgPvC4/YuJTQV736eNZ5JpHUn+jnYJg7OHN3RsKegxHOeAxJcB6OApeaWXa4zuuA3SPaJPrUJGMew4h7NatGbk9UET2hGkXTgV+E/7wGgEecc782s48AOOe+Q+gPwEfNbADoBm5PsD8MAJ8E/jv8lfog8IERx7CG0GiN/UAX8AGvCj2DsepP+HNgZtmEFpD5y2HvJdM5iOQYEvo8OOc2mNkThLqPBoAtwAOWRFOTRHgMnzKzVeHtJwmNnkl4ekJVRCQFaSikiEgKUriLiKQghbuISApSuIuIpCCFu4hIClK4S8ows++bWaOZ7Rij3dVmdtmw1182s9phs/79Y/j9581s1DU0zeyt4ekbtpnZLjP7y7PtSyTe4j3OXSSWfgh8E/jRGO2uBjqBl4a9d59z7huRfIiZZRBapedi51xN+HX1RPYlEiu6cpeU4Zz7AyMebTezT4WvrF81s8csNJ/4R4BPh6+sr4xk32bWaWZfMbMNwCWELoxOhD+31zm3J5rHIjJZCndJdZ8HLgxP+vQR59xh4DuErq6XOefWhdt9elhXyo2j7CcH2OGcuyT8j8hq4IiFFne408yG/10aa18iMadwl1T3KqGpFu4i9Pj4mQyF/TLn3NpRtg8CPxt64Zy7h9A8JK8AnwW+P459icScwl1S3Z8B9xOayW9zeGbCiehxzg0Of8M5t905dx+h+WHeObkyRaJL4S4pK9xVUuWcew74HFAI5AIdQN4k9ptrZlcPe2sZkIyrKEkK02gZSRlm9iihkTAlZlYDfBV4r5kVEFo04j7nXKuZPQU8YWa3Epohc9wfBXzOzL5LaLbGUyTJTIEydWhWSBGRFKRuGRGRFKRwFxFJQQp3EZEUpHAXEUlBCncRkRSkcBcRSUEKdxGRFKRwFxFJQf8f3VHQeiOOw4AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['1stFlrSF'].apply(np.log1p))\n",
    "df['1stFlrSF'].apply(np.log1p).skew()\n",
    "# df['1stFlrSF'] = df['1stFlrSF'].apply(np.log1p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    1529\n",
       "1    1309\n",
       "3      63\n",
       "0      12\n",
       "4       4\n",
       "Name: FullBath, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['FullBath'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FullBath\n",
       "0    165200.888889\n",
       "1    134751.440000\n",
       "2    213009.825521\n",
       "3    347822.909091\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby(['FullBath'])['SalePrice'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d1ca6fe908>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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n1wIfekU5RUHkJHYeyOTWsXPIzs1lwtBeNK4dH3ZJAiTVrca4IT1JP5LFbePmciAjK+ySilw0I43zgFuBi487vfYvZrbEzL4CLgJ+CuDuy4ApwHLgPeDuYESSDdwDTCdyMH1K0Bfgl8ADZpZK5JjFmKB9DFA/aH8A+O9puiIV1cHMbIaMn8v2/RmMva0nbRJqhF2S5NExsTbP39qD1B0HufPl+RzNLl83cLLy9od7cnKyp6SkhF2GSLE4mp3L0PHzmLV2Ny8O6sHFZzUKuyQ5gX/PT+PBfy3m6u5N+dv1pf+mV2Y2392TT9VP8wuIlBG5uc7P/rWYz1N38eS1XRQYpdw1PZLYmn6Ev77/NYl14vn55WeFXVKRUGiIlAHuzh/+dwVvL97CL/udxXXJzU69kITu7ovasHlfBs9+tIYmtatyS58WYZdUaAoNkTLghU/XMvaLdQw5ryV3XnBG2OVIlMyMxwZ0ZPv+DH771lIa14rnkg5le4Soq4BESrnX56fx+LSV/KBrIr/5XodSv29cvikuNoZ/DuxOp6a1uefVBSzaVLZv4KTQECnFPly5nV/++yvOb9OAv17XlRjdF6NMqlY5jjGDe5JQM55h4+exftehsEsqMIWGSCm1YONe7pq0gPZNavL8rT2oHKcf17KsYc0qjB/Sk1yP3MBp98HMsEsqEH0XipRCqTsOMHT8PBrVimfcbb10I6Vy4oyGNRg9uCdb0zMYOiGFI0fL3g2cFBoipcy29AwGjZlLXIwxcWgvGtasEnZJUoR6BBNLLknbx72vLiA7p2xd/KfQEClF0g9nMXjsXPZnZDN+SC9a1K8edklSDC7r2JhHf9CRD1bs4NF3lpWpGzhpzCtSSmRk5XDHxBTW7jrIhCG96NS0dtglSTEadE5LtuzL4PlP1pBYpyp3Xdgm7JKiotAQKQWyc3K579WFzNuwh2du7M65bRqEXZKUgF9c3o6t6Uf4y3uraFI7nqu7J4Vd0ikpNERC5u785q1lvL98O49e2YEruyaGXZKUkJgY4y/XdmHH/kx+8fpXJNSM57xS/geDjmmIhGzEB6t5de5G7rqwNbed1yrscqSEVYmL5flbe3BGgxrc+dJ8VmzdH3ZJJ6XQEAnRy7M38MzM1VyfnMTPL28XdjkSktpVKzFuSE+qV4njtnFz2bLvSNglnZBCQyQk05Zs5TdvLaXvWQn86erOmh6kgkusU5XxQ3tyODOH28bNJf1I6byBk0JDJASz1+7m/smL6N6sDv8ceDZxsfpRFDircS1euLUH63Yd4kcvpZCZXfou/tN3qkgJW75lP3dMSKF5/WqMva0nVSvHhl2SlCLnBvOMzV67h5/96ytyc0vXNRw6e0qkBG3ac5jB4+ZSvUocE4f2ok61ymGXJKXQgG5N2bIvgyfeW0linXge7t8+7JL+S6EhUkJ2H8xk8Ni5ZGbl8PqPzyWxTtWwS5JS7M4LzmDLviO88MlaEmtXZfC5LcMuCVBoiJSI9MNZDJ2QwuZ9R5h0e2/ObFQz7JKklDMzHv1BR7btz+DRd5bRqFY8/To1DrssHdMQKU7b92fw56krOO+JD1m6OZ1/Djyb5Jb1wi5LyojYGOOZG7vTNakO909eyPwNe8IuSaEhUhzW7TrEw298xXee+IgXP1vLxWcl8O6953NpGb/Vp5S8qpVjGTM4mSa14xk2IYU1Ow+GWo+VpdkVo5GcnOwpKSlhlyEV1JK0dJ7/ZA1Tl26lUmwM1ycnMfw7rWlev1rYpUkZt2H3IX743JdUqxLLGz8+r8inzDez+e6efKp+OqYhUkjuzpdrdjPy4zV8nrqLmvFxkSlBzm2le2FIkWlRvzpjb+vJjaNmM3T8PCYP70P1EG7OpdAQKaCcXOf9ZdsY+ckavkpLp2HNKjzc/ywG9m5OzfhKYZcn5VDXZnX458Du3DExhXteWcCLg5JL/MJQhYbIacrMzuE/CzfzwidrWbvrEC3rV+PPP+zM1d2bEl9JF+pJ8erbvhGPXdWJX725lN+8tbTEp6A5ZWiYWTNgItAYyAVGufvfzawe8BrQElgPXO/uey1S/d+BK4DDwG3uviBY12Dg18Gq/+DuE4L2HsB4oCowFbjf3f1E2yj0pxYpgIOZ2Uyeu5HRn61j2/4MOibW4tmBZ9OvU2NiYzRvlJScm3u3YOu+DP75USqJtatyb9+2JbbtaEYa2cCD7r7AzGoC881sBnAbMNPdHzezh4CHgF8C/YG2waM3MBLoHQTAI0Ay4MF63g5CYCQwHJhNJDT6AdOCdea3DZESs/tgJhO+XM+EWRtIP5LFua3r8+R1XTi/TQNNMiihefCyM9mSfoSnZnxN49rxXJfcrES2e8rQcPetwNbg+QEzWwE0BQYAFwbdJgAfE/mFPgCY6JHTsmabWR0zaxL0neHuewCC4OlnZh8Dtdx9VtA+EbiKSGicaBsixW7TnsOM/mwtr6VsIjM7l8s7NObOC1vTrVmdsEsTwcx4/IeRGzg9/MYSEmrFc8GZDYt9u6d1TMPMWgLdgTlAoyBQcPetZpYQdGsKbMqzWFrQdrL2tHzaOck2RIrNqm0HeP6TNby9eAsxBld3b8rw77amTUKNsEsT+YbKcTGMvOVsrnt+Fne9PJ/XfnROsd9bPurQMLMawL+Bn7j7/pMMy/N7wwvQHjUzG05k9xbNmzc/nUVF/itl/R5GfryGmSt3UK1yLEPObcmw77SiSW3NESWlV834SkwY2ot7X1lIfKXiP5MqqtAws0pEAmOSu78RNG83sybBCKAJsCNoTwPy7lxLArYE7Rce1/5x0J6UT/+TbeMb3H0UMAoiF/dF85lEjpm3fg9/eW8l89bvpW61Sjxw6ZkMOqeFZqCVMqNRrXhe+1GfEjnGdspYCs6GGgOscPe/5XnrbWBw8Hww8Fae9kEW0QdID3YxTQcuM7O6ZlYXuAyYHrx3wMz6BNsadNy68tuGSJH4YPl2Br44m817j/DolR344qGLua9vWwWGlDkldVJGNCON84BbgSVmtiho+x/gcWCKmQ0DNgLXBe9NJXK6bSqRU26HALj7HjN7DJgX9Pv9sYPiwI/5/1NupwUPTrINkUJ7f9k27n5lAR0SazNxaC9qV9UFeSKnormnpEJ6b+lW7nllIZ2TajNhaC9q6QpuqeCinXtKs9xKhTN1yVbufmUhXZIiIwwFhkj0NI2IVCjvLN7CT15bRPdmdRg/tBc1QpjwTaQs00hDKoy3Fm3m/skL6dG8rgJDpID0UyMVwpsL03hwymJ6tqzH2Nt6hjKltEh5oJ8cKff+PT+Nn72+mD6t6jPmtmSqVda3vUhB6adHyrV/pWziF//+ivNaN+DFQclUraypy0UKQ6Eh5daUeZv45RtfcX6bSGDoXhcihacD4VIuvTp3I7/491d8t21DBYZIEVJoSLnz8uwNPPzGEi5q15AXbu2hwBApQto9JeXKxFnr+e1by7ikfQLP3nw2VeIUGCJFSaEh5cb4L9bx6DvLubRDI54deDaV4zSQFilqCg0pF8Z8vo7H3l3O5R0b8Y+bFBgixUWhIWXei5+u5Y9TV3BF58b8/cbuVIpVYIgUF4WGlGnPf7KGx6et5HtdmvD0Dd0UGCLFTKEhZdazH6Xy5PRVXNk1kRHXdyVOgSFS7BQaUib9Y+ZqnprxNQO6JfLUdQoMkZKi0JAy5+kPvubpD1bzw+5NefK6rsTGlMxtLkVEoSFliLsz4oPVPDNzNdf2SOKJa7ooMERKmEJDygR3528zvuYfH6ZyfXISj/+wCzEKDJESp9CQUs/deXL6Kp77eA039WrGH6/qrMAQCYlCQ0o1d+fx91bywidrubl3cx4b0EmBIRIihYaUWtk5uTw+bSWjP1/HrX1a8PsBHTFTYIiESaEhpY678+HKHfx52kpSdxzktnNb8siVHRQYIqWAQkNKlSVp6fxx6nJmr91DqwbVef6WHlzesZECQ6SUUGhIqZC29zB/nb6K/yzaQr3qlfn9gI7c1Ku5pgURKWUUGhKq9CNZPPdxKuO+WI8Bd13YmjsvbE2t+EphlyYi+VBoSCiOZufy8uwN/OPD1ew7ksUPuyfx4GVnklinatilichJnHLsb2ZjzWyHmS3N0/aomW02s0XB44o87z1sZqlmtsrMLs/T3i9oSzWzh/K0tzKzOWa22sxeM7PKQXuV4HVq8H7LovrQEh53Z+qSrVw64hN+/+5yOiTW4t17z+ep67sqMETKgGh2GI8H+uXTPsLduwWPqQBm1gG4EegYLPOcmcWaWSzwLNAf6ADcFPQFeCJYV1tgLzAsaB8G7HX3NsCIoJ+UYfM37OGakV9y16QFxMfFMm5IT14e1puOibXDLk1EonTK3VPu/ulp/JU/AJjs7pnAOjNLBXoF76W6+1oAM5sMDDCzFcDFwMCgzwTgUWBksK5Hg/bXgX+ambm7R1mLlBLrdx3iifdWMm3pNhJqVuGJazpzbY9mmjdKpAwqzDGNe8xsEJACPOjue4GmwOw8fdKCNoBNx7X3BuoD+9w9O5/+TY8t4+7ZZpYe9N91fCFmNhwYDtC8efNCfCQpSnsOHeWZmat5efYGKsfF8NNLzuSO77aiWmUdShMpqwr60zsSeAzw4OtTwFAgvz8dnfx3g/lJ+nOK977Z6D4KGAWQnJyskUjIMrJyGP/lep79KJVDmdnc0LM5P720LQk148MuTUQKqUCh4e7bjz03sxeBd4OXaUCzPF2TgC3B8/zadwF1zCwuGG3k7X9sXWlmFgfUBvYUpF4pGbm5ztuLt/Dk9FVs3neEvmcl8FD/s2jbqGbYpYlIESlQaJhZE3ffGry8Gjh2ZtXbwCtm9jcgEWgLzCUyamhrZq2AzUQOlg90dzezj4BrgcnAYOCtPOsaDMwK3v9QxzNKry/X7OJPU1ewdPN+OjWtxZPXdeHc1g3CLktEitgpQ8PMXgUuBBqYWRrwCHChmXUjsrtoPfAjAHdfZmZTgOVANnC3u+cE67kHmA7EAmPdfVmwiV8Ck83sD8BCYEzQPgZ4KTiYvodI0Egps3r7AR6ftpKZK3fQtE5VRtzQlQFdm2omWpFyysrbH+/JycmekpISdhnl3o79GTw9czWT526keuU47rqoDUPOa0l8pdiwSxORAjCz+e6efKp+Oo1FTsu+w0d5/pO1jP9yHdk5zqBzWnJf37bUq1457NJEpAQoNCQqBzOzGff5OkZ9upaDR7MZ0DWRn1xyJi0bVA+7NBEpQQoNOamMrBwmzdnIcx+lsvvQUS7r0IgHL2tHu8Y6I0qkIlJoSL6ycnJ5fX4az8xczdb0DM5v04AHLzuT7s3rhl2aiIRIoSHfkJvrvLtkKyNmfM26XYfo3rwOT13XlXPb6PRZEVFoSMDdmbliB399fxUrtx3grMY1GT0omb7tE3TXPBH5L4WG8OWaXTw5fRULN+6jZf1q/P3GblzZJVHXWojItyg0KrBFm/bx1+mr+Dx1F41rxfPnH3bm2h5JusWqiJyQQqMCWrXtAE+9v4r3l2+nXvXK/Pp77bmlTwtdmCcip6TQqEA27D7E0x+s5j+LNlOjchwPXnomQ85vRY0q+jYQkejot0UFsC09g398uJrX5m0iLtb40Xdbc+cFZ1Cnmq7iFpHTo9Aox/YcOsrIj1OZOGsDue4M7N2cey5qQ0It3ddCRApGoVEOHcjIYvRn6xjz+ToOH83m6u5J/OSStjSrVy3s0kSkjFNolBMHMrL4cOUO3lu6jY9X7eRIVg79OzXmgUvP1E2QRKTIKDTKsPTDWcxYsZ33lm7l09W7OJqdS8OaVbi2RxLXJzejc1LtsEsUkXJGoVHG7D6YyfvLtzN1yVZmrdlNdq6TWDueW3q34IrOjTm7eV1dlCcixUahUQZs35/Be0u3MW3pVuau20OuQ4v61bj9O2fQv1NjuiTV1lQfIlIiFBqlVNrew0FQbGP+hr0AtE2owT0XtaFfpya0b1JTQSEiJU6hUYqs23WIaUu3Mm3JNpZsTgegQ5NaPHjpmfTv3Jg2CTqgLSLhUmiEyN1ZveMgU5ds5b2l21i57QAAXZvV4eH+Z9GvU2Na1Ned8USk9FBolDB3Z9mW/ZERxdJtrN15CDPo2aIev/1+B/p1akwlHGjtAAAOrElEQVRinaphlykiki+FRgnasu8It4yZw9qdh4iNMfqcUY8h57Xi8o6NSKipq7RFpPRTaJSgf3y4mrQ9R3jims5c2qEx9apr7icRKVsUGiUkbe9h/pWSxsDezbmhZ/OwyxERKRDdbaeEPPvRGmLM+PGFrcMuRUSkwBQaJWDzviO8Pn8TN/RsRpPaOsgtImXXKUPDzMaa2Q4zW5qnrZ6ZzTCz1cHXukG7mdkzZpZqZl+Z2dl5lhkc9F9tZoPztPcwsyXBMs9YcMXaibZRFj33USqARhkiUuZFM9IYD/Q7ru0hYKa7twVmBq8B+gNtg8dwYCREAgB4BOgN9AIeyRMCI4O+x5brd4ptlCmb9x1hSsomrk9uplNpRaTMO2VouPunwJ7jmgcAE4LnE4Cr8rRP9IjZQB0zawJcDsxw9z3uvheYAfQL3qvl7rPc3YGJx60rv22UKSM/jowy7rqoTciViIgUXkGPaTRy960AwdeEoL0psClPv7Sg7WTtafm0n2wbZcaWfUeYMi+N65Kb0VSjDBEpB4r6QHh+M+h5AdpPb6Nmw80sxcxSdu7cebqLF5uRH6/Bce7SsQwRKScKGhrbg11LBF93BO1pQLM8/ZKALadoT8qn/WTb+BZ3H+Xuye6e3LBhwwJ+pKK1Nf0Ir83bxLU9kkiqq9usikj5UNDQeBs4dgbUYOCtPO2DgrOo+gDpwa6l6cBlZlY3OAB+GTA9eO+AmfUJzpoadNy68ttGmfD8x2vIdeeuC3UsQ0TKj1NeEW5mrwIXAg3MLI3IWVCPA1PMbBiwEbgu6D4VuAJIBQ4DQwDcfY+ZPQbMC/r93t2PHVz/MZEztKoC04IHJ9lGqbctPYNXg1FGs3oaZYhI+XHK0HD3m07wVt98+jpw9wnWMxYYm097CtApn/bd+W2jLHj+kzXk5jp364wpESlndEV4Edu+P4NX5m7kmrM1yhCR8kehUcSe/2QNORpliEg5pdAoQjv2Z/DKnI38sHtTmtfXKENEyh+FRhF6/pO1ZOc691ysUYaIlE8KjSKy40AGk+Zs4OruTXVfbxEptxQaRWTUsVGGjmWISDmm0CgCOw9k8vKcDVzVrSktG2iUISLll0KjCIz6dA1Hs3N1LENEyj2FRiHtOpjJS7Mjo4xWGmWISDmn0CikUZ+u1ShDRCoMhUYh7DqYyUuzNjCgW1POaFgj7HJERIqdQqMQXvxsLZnZORpliEiFodAooN0HM5n45Qau7JpIa40yRKSCUGgU0IufrSMjO4d7L24bdikiIiVGoVEAew4dZeKs9VzZJZE2CRpliEjFodAogNGfreVIVg739dWxDBGpWBQap2nvoaNM+HI93++SSJuEmmGXIyJSohQap2n052s5nJXDfTpjSkQqIIXGaYiMMjZwRecmtG2kUYaIVDwKjdMw5vN1HDqazX06Y0pEKiiFRpT2HT7K+C/Xc0WnJrRrrFGGiFRMCo0ojf18HQczs7lXZ0yJSAWm0IhC+uEsxn2xnv6dGnNW41phlyMiEhqFRhTGfLGOA5nZ3NdXxzJEpGJTaJxC+pEsxn2xjn4dG9O+iUYZIlKxKTROYdwX6ziQoVGGiAgoNE4q/UgWYz5fx+UdG9EhUaMMEZFChYaZrTezJWa2yMxSgrZ6ZjbDzFYHX+sG7WZmz5hZqpl9ZWZn51nP4KD/ajMbnKe9R7D+1GBZK0y9p2v8F+s1yhARyaMoRhoXuXs3d08OXj8EzHT3tsDM4DVAf6Bt8BgOjIRIyACPAL2BXsAjx4Im6DM8z3L9iqDeqOzPyGLM52u5tEMjOibWLqnNioiUasWxe2oAMCF4PgG4Kk/7RI+YDdQxsybA5cAMd9/j7nuBGUC/4L1a7j7L3R2YmGddxW78F+vZn5HN/RpliIj8V2FDw4H3zWy+mQ0P2hq5+1aA4GtC0N4U2JRn2bSg7WTtafm0f4uZDTezFDNL2blzZyE/EhzIiBzLuKR9Izo11ShDROSYuEIuf567bzGzBGCGma08Sd/8jkd4Adq/3eg+ChgFkJycnG+f0zHhy/WkH8nSKENE5DiFGmm4+5bg6w7gTSLHJLYHu5YIvu4IuqcBzfIsngRsOUV7Uj7txepARhajP1/HJe0T6JykUYaISF4FDg0zq25mNY89By4DlgJvA8fOgBoMvBU8fxsYFJxF1QdID3ZfTQcuM7O6wQHwy4DpwXsHzKxPcNbUoDzrKjYTZ21g3+Es7u97ZnFvSkSkzCnM7qlGwJvBWbBxwCvu/p6ZzQOmmNkwYCNwXdB/KnAFkAocBoYAuPseM3sMmBf0+7277wme/xgYD1QFpgWPYnMwM5sXP1vLxWdplCEikp8Ch4a7rwW65tO+G+ibT7sDd59gXWOBsfm0pwCdClrj6Zo4a30wytCxDBGR/OiK8MChzGxe/HQtF7VrSNdmdcIuR0SkVFJoBCbO2sDew1ncf4mOZYiInIhCI9CwZhWuT06im0YZIiInVNjrNMqNa3skcW2PpFN3FBGpwDTSEBGRqCk0REQkagoNERGJmkJDRESiptAQEZGoKTRERCRqCg0REYmaQkNERKJmkXkEyw8z2wlsKODiDYBdRVhOmPRZSp/y8jlAn6W0KsxnaeHuDU/VqdyFRmGYWYq7J4ddR1HQZyl9ysvnAH2W0qokPot2T4mISNQUGiIiEjWFxjeNCruAIqTPUvqUl88B+iylVbF/Fh3TEBGRqGmkISIiUVNoAGbWzMw+MrMVZrbMzO4Pu6bCMLNYM1toZu+GXUthmFkdM3vdzFYG/zfnhF1TQZnZT4PvraVm9qqZxYddU7TMbKyZ7TCzpXna6pnZDDNbHXytG2aN0TrBZ3ky+B77yszeNLNSfye2/D5Hnvd+ZmZuZg2KY9sKjYhs4EF3bw/0Ae42sw4h11QY9wMrwi6iCPwdeM/dzwK6UkY/k5k1Be4Dkt29ExAL3BhuVadlPNDvuLaHgJnu3haYGbwuC8bz7c8yA+jk7l2Ar4GHS7qoAhjPtz8HZtYMuBTYWFwbVmgA7r7V3RcEzw8Q+eXUNNyqCsbMkoDvAaPDrqUwzKwW8F1gDIC7H3X3feFWVShxQFUziwOqAVtCridq7v4psOe45gHAhOD5BOCqEi2qgPL7LO7+vrtnBy9nA6X+Fp4n+D8BGAH8Aii2g9UKjeOYWUugOzAn3EoK7Gki3zS5YRdSSGcAO4Fxwa620WZWPeyiCsLdNwN/JfLX31Yg3d3fD7eqQmvk7lsh8kcXkBByPUVlKDAt7CIKwsx+AGx298XFuR2FRh5mVgP4N/ATd98fdj2ny8y+D+xw9/lh11IE4oCzgZHu3h04RNnZBfINwf7+AUArIBGobma3hFuVHM/MfkVkV/WksGs5XWZWDfgV8Nvi3pZCI2BmlYgExiR3fyPsegroPOAHZrYemAxcbGYvh1tSgaUBae5+bMT3OpEQKYsuAda5+053zwLeAM4NuabC2m5mTQCCrztCrqdQzGww8H3gZi+b1yG0JvJHyeLg5z8JWGBmjYt6QwoNwMyMyL7zFe7+t7DrKSh3f9jdk9y9JZEDrR+6e5n8i9bdtwGbzKxd0NQXWB5iSYWxEehjZtWC77W+lNGD+nm8DQwOng8G3gqxlkIxs37AL4EfuPvhsOspCHdf4u4J7t4y+PlPA84Ofo6KlEIj4jzgViJ/mS8KHleEXZRwLzDJzL4CugF/CrmeAglGS68DC4AlRH7uysxVyGb2KjALaGdmaWY2DHgcuNTMVhM5W+fxMGuM1gk+yz+BmsCM4Gf/+VCLjMIJPkfJbLtsjsRERCQMGmmIiEjUFBoiIhI1hYaIiERNoSEiIlFTaIiISNQUGlIumVn9PKdPbzOzzXleV86nfz0zuzPP6zZmdiTov8LMxgfzRhVVff9rZp8d1/aymZ3WHE5mdoWZzQtmaV0UzKB7yrmTzCzOzMryXF4SEoWGlEvuvtvdu7l7N+B5YMSx1+5+NJ9F6gF3Hte2Kli+M5Grba8pitrMrH6wzkZm1rwQ6+lKZK6xW4KZgLsDrwEt8ulbZIEnFZtCQyocM/tFcF+LpWZ2b9D8OJELpRaZ2TcuVAtmQJ1HMPOxmd1uZm+Y2btmts7MfmxmPw8mVvzy2P0YgntoLDezxcdN53It8B8iv+BvOK68y83sMzP72sz6B+tJyXNlPGb2eRAYDwGPufuqoE539/+4+xd5+v3RzD4F7jGz1mY2x8zmAY8W/l9SKiKFhlQoZtYLuBnoBZwD3GVmXYj8Al4VjEQeOm6ZqkBPYHqe5o5EfuH3AZ4A9gYTK84Hjk3d8gugm7t3Be7Js+xNwKvB46bjSmwGXABcCYwysypEwuX6oJYkoH4wk2lHIleZn0wtd/+uuz8N/AP4u7v3JDKDsMhpU2hIRfMd4N/ufji4d8p/gPNP0LedmS0CdgOp7r4sz3sfuvshd98OHATeCdqXAC2D58uAl83sZiAL/ntDpubAbHdfDsSa2Vl51jvF3XOD0cMmoC0wBbgueP+G4PU3mFlCMEpabWY/yfPW5DzPzyESQAAvneAzi5yUQkMqGjuNvseOabQBLjhuPrLMPM9z87zOJTKtO8DlRI6n9AJSzCyWyC/9+sC6YDbS5nzzLn7Hz+vj7r4BOBjcTfIG/v8X/zKCmX/dfUdQ6xigRp7lDx23bs0bJIWi0JCK5lPgajOrGtw/ZQDwGXCAyKR13+LuW4jcAjTq24AGAZHk7h8CPwcaErlj303AJXlmI+3FN3dRXWcRZxLZVbU6aH8t2H6VYIQC8Bfgt3mPdwTbOJHZBLu5iOyiEzltCg2pUNx9LpFjCfOI/BIdGUwrvZ3IaGDJ8QfCA68D9czsnCg3FQe8EszQu4DIcY8EoDGQkqee1UCmmfUImlKJBNs7wPA8Z3r9CxhInl1T7r4QeCDYzioz+4LIqCjvLqm87gN+amZz+eZoRCRqmuVWRESippGGiIhETaEhIiJRU2iIiEjUFBoiIhI1hYaIiERNoSEiIlFTaIiISNQUGiIiErX/A7iDqBwW9bS5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.groupby('TotRmsAbvGrd')['SalePrice'].mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d1ca774c88>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.groupby(['YearBuilt'])['SalePrice'].count().plot()\n",
    "# train.groupby(['YearBuilt'])['SalePrice'].mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# train.groupby('YearRemodAdd')['SalePrice'].count().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d1ca83e978>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['YearRemodAdd-YearBuilt'] = df['YearRemodAdd'].apply(np.int) - df['YearBuilt'].apply(np.int)\n",
    "df.groupby('YearRemodAdd-YearBuilt')['SalePrice'].count().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['MasVnrArea'].fillna(0).apply(np.log1p))\n",
    "# df['MasVnrArea'].value_counts()\n",
    "# df['MasVnrArea'].fillna(0).apply(np.log1p).skew()\n",
    "df['MasVnrArea'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d1ca697160>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['LotArea'] = df['LotArea'].apply(np.log1p)\n",
    "sns.distplot(df['LotArea'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Overall'] = df['OverallCond'] * df['OverallQual']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['NumOfBath'] = df['BsmtFullBath'] + df['BsmtHalfBath']*0.5 + df['FullBath'] + df['HalfBath']*0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['PorchSF'] = df['OpenPorchSF'] + df['EnclosedPorch'] + df['3SsnPorch'] + df['ScreenPorch']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal     2402\n",
      "Partial     243\n",
      "Abnorml     190\n",
      "Family       46\n",
      "Alloca       24\n",
      "AdjLand      12\n",
      "Name: SaleCondition, dtype: int64\n",
      "\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "feature = 'SaleCondition'\n",
    "print(df[feature].value_counts())\n",
    "print(\"\")\n",
    "print(df[feature].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>YearRemodAdd-YearBuilt</th>\n",
       "      <th>Overall</th>\n",
       "      <th>NumOfBath</th>\n",
       "      <th>PorchSF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>9.042040</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>196.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>706.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>856.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>856</td>\n",
       "      <td>854</td>\n",
       "      <td>0</td>\n",
       "      <td>1710</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>8</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2.0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500.0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>3.5</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.169623</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>978.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>1262.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1262</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500.0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>9.328212</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>162.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Mn</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>486.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>920.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>0</td>\n",
       "      <td>1786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2.0</td>\n",
       "      <td>608.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500.0</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>3.5</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9.164401</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Shng</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>BrkTil</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>216.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>756.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>961</td>\n",
       "      <td>756</td>\n",
       "      <td>0</td>\n",
       "      <td>1717</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>3.0</td>\n",
       "      <td>642.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000.0</td>\n",
       "      <td>55</td>\n",
       "      <td>35</td>\n",
       "      <td>2.0</td>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>9.565284</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>350.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Av</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>655.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>0</td>\n",
       "      <td>2198</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>9</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>3.0</td>\n",
       "      <td>836.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>3.5</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id MSSubClass MSZoning  LotFrontage   LotArea Street Alley LotShape  \\\n",
       "0   1         60       RL         65.0  9.042040   Pave   NaN      Reg   \n",
       "1   2         20       RL         80.0  9.169623   Pave   NaN      Reg   \n",
       "2   3         60       RL         68.0  9.328212   Pave   NaN      IR1   \n",
       "3   4         70       RL         60.0  9.164401   Pave   NaN      IR1   \n",
       "4   5         60       RL         84.0  9.565284   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities LotConfig LandSlope Neighborhood Condition1  \\\n",
       "0         Lvl    AllPub    Inside       Gtl      CollgCr       Norm   \n",
       "1         Lvl    AllPub       FR2       Gtl      Veenker      Feedr   \n",
       "2         Lvl    AllPub    Inside       Gtl      CollgCr       Norm   \n",
       "3         Lvl    AllPub    Corner       Gtl      Crawfor       Norm   \n",
       "4         Lvl    AllPub       FR2       Gtl      NoRidge       Norm   \n",
       "\n",
       "  Condition2 BldgType HouseStyle  OverallQual  OverallCond YearBuilt  \\\n",
       "0       Norm     1Fam     2Story            7            5      2003   \n",
       "1       Norm     1Fam     1Story            6            8      1976   \n",
       "2       Norm     1Fam     2Story            7            5      2001   \n",
       "3       Norm     1Fam     2Story            7            5      1915   \n",
       "4       Norm     1Fam     2Story            8            5      2000   \n",
       "\n",
       "  YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType  \\\n",
       "0         2003     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "1         1976     Gable  CompShg     MetalSd     MetalSd       None   \n",
       "2         2002     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "3         1970     Gable  CompShg     Wd Sdng     Wd Shng       None   \n",
       "4         2000     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "\n",
       "   MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure  \\\n",
       "0       196.0        Gd        TA      PConc       Gd       TA           No   \n",
       "1         0.0        TA        TA     CBlock       Gd       TA           Gd   \n",
       "2       162.0        Gd        TA      PConc       Gd       TA           Mn   \n",
       "3         0.0        TA        TA     BrkTil       TA       Gd           No   \n",
       "4       350.0        Gd        TA      PConc       Gd       TA           Av   \n",
       "\n",
       "  BsmtFinType1  BsmtFinSF1 BsmtFinType2  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  \\\n",
       "0          GLQ       706.0          Unf         0.0      150.0        856.0   \n",
       "1          ALQ       978.0          Unf         0.0      284.0       1262.0   \n",
       "2          GLQ       486.0          Unf         0.0      434.0        920.0   \n",
       "3          ALQ       216.0          Unf         0.0      540.0        756.0   \n",
       "4          GLQ       655.0          Unf         0.0      490.0       1145.0   \n",
       "\n",
       "  Heating HeatingQC CentralAir Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  \\\n",
       "0    GasA        Ex          Y      SBrkr       856       854             0   \n",
       "1    GasA        Ex          Y      SBrkr      1262         0             0   \n",
       "2    GasA        Ex          Y      SBrkr       920       866             0   \n",
       "3    GasA        Gd          Y      SBrkr       961       756             0   \n",
       "4    GasA        Ex          Y      SBrkr      1145      1053             0   \n",
       "\n",
       "   GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  \\\n",
       "0       1710           1.0           0.0         2         1             3   \n",
       "1       1262           0.0           1.0         2         0             3   \n",
       "2       1786           1.0           0.0         2         1             3   \n",
       "3       1717           1.0           0.0         1         0             3   \n",
       "4       2198           1.0           0.0         2         1             4   \n",
       "\n",
       "   KitchenAbvGr KitchenQual  TotRmsAbvGrd Functional  Fireplaces FireplaceQu  \\\n",
       "0             1          Gd             8        Typ           0         NaN   \n",
       "1             1          TA             6        Typ           1          TA   \n",
       "2             1          Gd             6        Typ           1          TA   \n",
       "3             1          Gd             7        Typ           1          Gd   \n",
       "4             1          Gd             9        Typ           1          TA   \n",
       "\n",
       "  GarageType GarageYrBlt GarageFinish  GarageCars  GarageArea GarageQual  \\\n",
       "0     Attchd      2003.0          RFn         2.0       548.0         TA   \n",
       "1     Attchd      1976.0          RFn         2.0       460.0         TA   \n",
       "2     Attchd      2001.0          RFn         2.0       608.0         TA   \n",
       "3     Detchd      1998.0          Unf         3.0       642.0         TA   \n",
       "4     Attchd      2000.0          RFn         3.0       836.0         TA   \n",
       "\n",
       "  GarageCond PavedDrive  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  \\\n",
       "0         TA          Y           0           61              0          0   \n",
       "1         TA          Y         298            0              0          0   \n",
       "2         TA          Y           0           42              0          0   \n",
       "3         TA          Y           0           35            272          0   \n",
       "4         TA          Y         192           84              0          0   \n",
       "\n",
       "   ScreenPorch  PoolArea PoolQC Fence MiscFeature  MiscVal  MoSold YrSold  \\\n",
       "0            0         0    NaN   NaN         NaN        0       2   2008   \n",
       "1            0         0    NaN   NaN         NaN        0       5   2007   \n",
       "2            0         0    NaN   NaN         NaN        0       9   2008   \n",
       "3            0         0    NaN   NaN         NaN        0       2   2006   \n",
       "4            0         0    NaN   NaN         NaN        0      12   2008   \n",
       "\n",
       "  SaleType SaleCondition  SalePrice  YearRemodAdd-YearBuilt  Overall  \\\n",
       "0       WD        Normal   208500.0                       0       35   \n",
       "1       WD        Normal   181500.0                       0       48   \n",
       "2       WD        Normal   223500.0                       1       35   \n",
       "3       WD       Abnorml   140000.0                      55       35   \n",
       "4       WD        Normal   250000.0                       0       40   \n",
       "\n",
       "   NumOfBath  PorchSF  \n",
       "0        3.5       61  \n",
       "1        2.5        0  \n",
       "2        3.5       42  \n",
       "3        2.0      307  \n",
       "4        3.5       84  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train['Overall'] = train['OverallCond'] * train['OverallQual']\n",
    "# train.corr().sort_values(by=['SalePrice'], ascending=False)['SalePrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalePrice                 1.000000\n",
       "OverallQual               0.795774\n",
       "GrLivArea                 0.734968\n",
       "TotalBsmtSF               0.651153\n",
       "GarageCars                0.641047\n",
       "NumOfBath                 0.635896\n",
       "1stFlrSF                  0.631530\n",
       "GarageArea                0.629217\n",
       "Overall                   0.566759\n",
       "FullBath                  0.562165\n",
       "TotRmsAbvGrd              0.537769\n",
       "MasVnrArea                0.477810\n",
       "Fireplaces                0.469862\n",
       "BsmtFinSF1                0.409384\n",
       "LotArea                   0.392170\n",
       "LotFrontage               0.370584\n",
       "WoodDeckSF                0.324758\n",
       "OpenPorchSF               0.321142\n",
       "2ndFlrSF                  0.320532\n",
       "HalfBath                  0.284590\n",
       "BsmtFullBath              0.228459\n",
       "BsmtUnfSF                 0.214460\n",
       "PorchSF                   0.196875\n",
       "BedroomAbvGr              0.168245\n",
       "ScreenPorch               0.111415\n",
       "PoolArea                  0.099490\n",
       "MoSold                    0.046124\n",
       "3SsnPorch                 0.044568\n",
       "BsmtFinSF2               -0.011422\n",
       "BsmtHalfBath             -0.016881\n",
       "MiscVal                  -0.021203\n",
       "Id                       -0.021673\n",
       "LowQualFinSF             -0.025625\n",
       "OverallCond              -0.077948\n",
       "EnclosedPorch            -0.128646\n",
       "KitchenAbvGr             -0.135946\n",
       "YearRemodAdd-YearBuilt   -0.217635\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr().sort_values(by=['SalePrice'], ascending=False).SalePrice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig,axes=plt.subplots(2,2)\n",
    "# ax1=axes[0,0]\n",
    "# ax2=axes[0,1]\n",
    "# ax3=axes[1,0]\n",
    "# ax4=axes[1,1]\n",
    "\n",
    "# fig = plt.figure(figsize=(10,15))\n",
    "# ax1 = fig.add_subplot(3,1,1)\n",
    "# sns.distplot(df['LotFrontage'].fillna(0))\n",
    "# ax2 = fig.add_subplot(3,1,2)\n",
    "# sns.distplot(df['LotFrontage'].fillna(0).apply(np.log1p))\n",
    "# ax3 = fig.add_subplot(3,1,3)\n",
    "# stats.probplot(df['LotFrontage'].fillna(0).apply(np.log1p), plot=ax3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>65.0</td>\n",
       "      <td>9.042040</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>196.0</td>\n",
       "      <td>706.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>856.0</td>\n",
       "      <td>856</td>\n",
       "      <td>854</td>\n",
       "      <td>0</td>\n",
       "      <td>1710</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>208500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.169623</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>1262.0</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1262</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>181500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>68.0</td>\n",
       "      <td>9.328212</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>162.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>920.0</td>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>0</td>\n",
       "      <td>1786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>608.0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>223500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9.164401</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>756.0</td>\n",
       "      <td>961</td>\n",
       "      <td>756</td>\n",
       "      <td>0</td>\n",
       "      <td>1717</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>642.0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>140000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>84.0</td>\n",
       "      <td>9.565284</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>350.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>0</td>\n",
       "      <td>2198</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>836.0</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>250000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  LotFrontage   LotArea  OverallQual  OverallCond  MasVnrArea  \\\n",
       "0   1         65.0  9.042040            7            5       196.0   \n",
       "1   2         80.0  9.169623            6            8         0.0   \n",
       "2   3         68.0  9.328212            7            5       162.0   \n",
       "3   4         60.0  9.164401            7            5         0.0   \n",
       "4   5         84.0  9.565284            8            5       350.0   \n",
       "\n",
       "   BsmtFinSF1  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  1stFlrSF  2ndFlrSF  \\\n",
       "0       706.0         0.0      150.0        856.0       856       854   \n",
       "1       978.0         0.0      284.0       1262.0      1262         0   \n",
       "2       486.0         0.0      434.0        920.0       920       866   \n",
       "3       216.0         0.0      540.0        756.0       961       756   \n",
       "4       655.0         0.0      490.0       1145.0      1145      1053   \n",
       "\n",
       "   LowQualFinSF  GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  \\\n",
       "0             0       1710           1.0           0.0         2         1   \n",
       "1             0       1262           0.0           1.0         2         0   \n",
       "2             0       1786           1.0           0.0         2         1   \n",
       "3             0       1717           1.0           0.0         1         0   \n",
       "4             0       2198           1.0           0.0         2         1   \n",
       "\n",
       "   BedroomAbvGr  KitchenAbvGr  TotRmsAbvGrd  Fireplaces  GarageCars  \\\n",
       "0             3             1             8           0         2.0   \n",
       "1             3             1             6           1         2.0   \n",
       "2             3             1             6           1         2.0   \n",
       "3             3             1             7           1         3.0   \n",
       "4             4             1             9           1         3.0   \n",
       "\n",
       "   GarageArea  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  \\\n",
       "0       548.0           0           61              0          0            0   \n",
       "1       460.0         298            0              0          0            0   \n",
       "2       608.0           0           42              0          0            0   \n",
       "3       642.0           0           35            272          0            0   \n",
       "4       836.0         192           84              0          0            0   \n",
       "\n",
       "   PoolArea  MiscVal  MoSold  SalePrice  \n",
       "0         0        0       2   208500.0  \n",
       "1         0        0       5   181500.0  \n",
       "2         0        0       9   223500.0  \n",
       "3         0        0       2   140000.0  \n",
       "4         0        0      12   250000.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[num_cols].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\sklearn\\grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.grid_search import GridSearchCV \n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "\n",
    "import lightgbm as lgb\n",
    "from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_copy = df.copy()\n",
    "df_y = np.log1p(df_copy['SalePrice'])\n",
    "df_copy.drop(['Id', 'SalePrice'], axis=1, inplace=True)\n",
    "df_x = pd.get_dummies(df_copy)\n",
    "x = df_x[:len(train)-2]\n",
    "y = df_y[:len(train)-2]\n",
    "test_x = df_x[len(train)-2:]\n",
    "\n",
    "train_x, valid_x, train_y, valid_y = train_test_split(x, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
       "       importance_type='split', learning_rate=0.01, max_depth=-1,\n",
       "       min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
       "       n_estimators=5000, n_jobs=-1, num_leaves=31, objective='regression',\n",
       "       random_state=None, reg_alpha=0.0, reg_lambda=0.0, silent=True,\n",
       "       subsample=1.0, subsample_for_bin=200000, subsample_freq=0,\n",
       "       verbose=-1),\n",
       "       fit_params={}, iid=True, n_jobs=-1,\n",
       "       param_grid={'max_depth': [4], 'num_leaves': [3], 'feature_fraction': [0.2], 'cat_smooth': [0], 'bagging_fraction': [0.9], 'bagging_freq': [3]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=0)"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paramaters = {\n",
    "    'max_depth':[4],\n",
    "    'num_leaves':[3],\n",
    "    'feature_fraction': [0.2],\n",
    "    'cat_smooth': [1],\n",
    "    'bagging_fraction':[0.9],\n",
    "    'bagging_freq': [3],\n",
    "    'cat_smooth':[0]\n",
    "    \n",
    "}\n",
    "\n",
    "gbm = lgb.LGBMRegressor(\n",
    "                    objective='regression',\n",
    "                    learning_rate = 0.01,\n",
    "                    n_estimators=5000,\n",
    "                    verbose = -1\n",
    ")\n",
    "\n",
    "gscv = GridSearchCV(gbm,\n",
    "                    param_grid=paramaters,\n",
    "                    cv=5,\n",
    "                    n_jobs=-1\n",
    "                   )\n",
    "gscv.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bagging_fraction': 0.9,\n",
       " 'bagging_freq': 3,\n",
       " 'cat_smooth': 0,\n",
       " 'feature_fraction': 0.2,\n",
       " 'max_depth': 4,\n",
       " 'num_leaves': 3}"
      ]
     },
     "execution_count": 396,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9128955020190656"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08819168638331422"
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(np.sum(np.power((gscv.best_estimator_.predict(valid_x) - valid_y),2))/len(valid_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "__init__() missing 1 required positional argument: 'param_grid'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-39-a1ae5f450842>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     14\u001b[0m gscv_xg = GridSearchCV(xgboost,\n\u001b[0;32m     15\u001b[0m                       \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m                       n_jobs=-1)\n\u001b[0m\u001b[0;32m     17\u001b[0m \u001b[0mgscv_xg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: __init__() missing 1 required positional argument: 'param_grid'"
     ]
    }
   ],
   "source": [
    "params = {\n",
    "    max_depth=[3,4,5]\n",
    "}\n",
    "xgboost = XGBRegressor(learning_rate=0.01,\n",
    "                       n_estimators=3460,\n",
    "                       min_child_weight=0,\n",
    "                       gamma = 0,\n",
    "                       subsample=0.7,\n",
    "                       colsample_bytree=0.7,\n",
    "                       objective='reg:linear',\n",
    "                       nthread=-1,\n",
    "                       scale_pos_weight=1,\n",
    "                       seed=27,\n",
    "                       reg_alpha=0.00006,\n",
    "                       random_state=42)\n",
    "gscv_xg = GridSearchCV(xgboost,\n",
    "                       cv=5,\n",
    "                       param_grid=params\n",
    "                       n_jobs=-1)\n",
    "gscv_xg.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = gscv.predict(test_x)\n",
    "submission = pd.DataFrame({'Id':test.Id, 'SalePrice':np.expm1(pred)})\n",
    "submission.to_csv('submission_2020_02_19.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score, train_test_split, KFold\n",
    "from lightgbm import LGBMRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.log1p(df['SalePrice'][:len(train)])\n",
    "df.drop(['Id', 'SalePrice'], axis=1, inplace=True)\n",
    "df = pd.get_dummies(df)\n",
    "X_train = df[:len(train)]\n",
    "X_test = df[len(train):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.125241255874531 0.01098852762372987\n"
     ]
    }
   ],
   "source": [
    "kfolds = KFold(n_splits=10, shuffle=True, random_state=42)\n",
    "\n",
    "def rmse_cv(model):\n",
    "    rmse = np.sqrt(-cross_val_score(model, X_train, y, scoring='neg_mean_squared_error', cv=5))\n",
    "    return rmse\n",
    "    \n",
    "lightgbm = LGBMRegressor(objective='regression',\n",
    "                        num_levels=4,\n",
    "                        learning_rate=0.01,\n",
    "                        n_estimators=5000,\n",
    "                        bagging_freq=5,\n",
    "                        bagging_seed=7,\n",
    "                        feature_fraction=0.2,\n",
    "                        feature_fraction_seed=7,\n",
    "                        verbose=-1)\n",
    "\n",
    "score = rmse_cv(lightgbm)\n",
    "print(score.mean(), score.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bagging_fraction': 0.2,\n",
       " 'bagging_freq': 5,\n",
       " 'bagging_seed': 5,\n",
       " 'cat_smooth': 1,\n",
       " 'feature_fraction': 0.2,\n",
       " 'feature_fraction_seed': 3,\n",
       " 'learning_rate': 0.01,\n",
       " 'max_depth': 5}"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11.71469811, 12.00367922, 12.1460504 , ..., 12.02656319,\n",
       "       11.65985617, 12.28136353])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lightgbm = lightgbm.fit(X_train, y)\n",
    "pred = lightgbm.predict(X_test)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({'Id':test.Id, 'SalePrice':np.expm1(pred)})\n",
    "submission.to_csv('submission_2020_02_16.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.subplots(figsize=(30, 20))\n",
    "mask = np.zeros_like(train.corr(), dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "sns.heatmap(train.corr(), annot=True, mask=mask)\n",
    "# 设置坐标轴字体大小\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)"
   ]
  }
 ],
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